case study example data science

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Top 10 real-world data science case studies.

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Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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2024 Guide: 20+ Essential Data Science Case Study Interview Questions

Case studies are often the most challenging aspect of data science interview processes. They are crafted to resemble a company’s existing or previous projects, assessing a candidate’s ability to tackle prompts, convey their insights, and navigate obstacles.

To excel in data science case study interviews, practice is crucial. It will enable you to develop strategies for approaching case studies, asking the right questions to your interviewer, and providing responses that showcase your skills while adhering to time constraints.

The best way of doing this is by using a framework for answering case studies. For example, you could use the product metrics framework and the A/B testing framework to answer most case studies that come up in data science interviews.

There are four main types of data science case studies:

• Product Case Studies - This type of case study tackles a specific product or feature offering, often tied to the interviewing company. Interviewers are generally looking for a sense of business sense geared towards product metrics.
• Data Analytics Case Study Questions - Data analytics case studies ask you to propose possible metrics in order to investigate an analytics problem. Additionally, you must write a SQL query to pull your proposed metrics, and then perform analysis using the data you queried, just as you would do in the role.
• Modeling and Machine Learning Case Studies - Modeling case studies are more varied and focus on assessing your intuition for building models around business problems.
• Business Case Questions - Similar to product questions, business cases tackle issues or opportunities specific to the organization that is interviewing you. Often, candidates must assess the best option for a certain business plan being proposed, and formulate a process for solving the specific problem.

How Case Study Interviews Are Conducted

Oftentimes as an interviewee, you want to know the setting and format in which to expect the above questions to be asked. Unfortunately, this is company-specific: Some prefer real-time settings, where candidates actively work through a prompt after receiving it, while others offer some period of days (say, a week) before settling in for a presentation of your findings.

It is therefore important to have a system for answering these questions that will accommodate all possible formats, such that you are prepared for any set of circumstances (we provide such a framework below).

Why Are Case Study Questions Asked?

Case studies assess your thought process in answering data science questions. Specifically, interviewers want to see that you have the ability to think on your feet, and to work through real-world problems that likely do not have a right or wrong answer. Real-world case studies that are affecting businesses are not binary; there is no black-and-white, yes-or-no answer. This is why it is important that you can demonstrate decisiveness in your investigations, as well as show your capacity to consider impacts and topics from a variety of angles. Once you are in the role, you will be dealing directly with the ambiguity at the heart of decision-making.

Perhaps most importantly, case interviews assess your ability to effectively communicate your conclusions. On the job, data scientists exchange information across teams and divisions, so a significant part of the interviewer’s focus will be on how you process and explain your answer.

Quick tip: Because case questions in data science interviews tend to be product- and company-focused, it is extremely beneficial to research current projects and developments across different divisions , as these initiatives might end up as the case study topic.

How to Answer Data Science Case Study Questions (The Framework)

There are four main steps to tackling case questions in Data Science interviews, regardless of the type: clarify, make assumptions, gather context, and provide data points and analysis.

Step 1: Clarify

Clarifying is used to gather more information . More often than not, these case studies are designed to be confusing and vague. There will be unorganized data intentionally supplemented with extraneous or omitted information, so it is the candidate’s responsibility to dig deeper, filter out bad information, and fill gaps. Interviewers will be observing how an applicant asks questions and reach their solution.

For example, with a product question, you might take into consideration:

• What is the product?
• How does the product work?
• How does the product align with the business itself?

Step 2: Make Assumptions

When you have made sure that you have evaluated and understand the dataset, start investigating and discarding possible hypotheses. Developing insights on the product at this stage complements your ability to glean information from the dataset, and the exploration of your ideas is paramount to forming a successful hypothesis. You should be communicating your hypotheses with the interviewer, such that they can provide clarifying remarks on how the business views the product, and to help you discard unworkable lines of inquiry. If we continue to think about a product question, some important questions to evaluate and draw conclusions from include:

• Who uses the product? Why?
• What are the goals of the product?
• How does the product interact with other services or goods the company offers?

The goal of this is to reduce the scope of the problem at hand, and ask the interviewer questions upfront that allow you to tackle the meat of the problem instead of focusing on less consequential edge cases.

Step 3: Propose a Solution

Now that a hypothesis is formed that has incorporated the dataset and an understanding of the business-related context, it is time to apply that knowledge in forming a solution. Remember, the hypothesis is simply a refined version of the problem that uses the data on hand as its basis to being solved. The solution you create can target this narrow problem, and you can have full faith that it is addressing the core of the case study question.

Keep in mind that there isn’t a single expected solution, and as such, there is a certain freedom here to determine the exact path for investigation.

Step 4: Provide Data Points and Analysis

Finally, providing data points and analysis in support of your solution involves choosing and prioritizing a main metric. As with all prior factors, this step must be tied back to the hypothesis and the main goal of the problem. From that foundation, it is important to trace through and analyze different examples– from the main metric–in order to validate the hypothesis.

Quick tip: Every case question tends to have multiple solutions. Therefore, you should absolutely consider and communicate any potential trade-offs of your chosen method. Be sure you are communicating the pros and cons of your approach.

Note: In some special cases, solutions will also be assessed on the ability to convey information in layman’s terms. Regardless of the structure, applicants should always be prepared to solve through the framework outlined above in order to answer the prompt.

The Role of Effective Communication

There have been multiple articles and discussions conducted by interviewers behind the Data Science Case Study portion, and they all boil down success in case studies to one main factor: effective communication.

All the analysis in the world will not help if interviewees cannot verbally work through and highlight their thought process within the case study. Again, interviewers are keyed at this stage of the hiring process to look for well-developed “soft-skills” and problem-solving capabilities. Demonstrating those traits is key to succeeding in this round.

To this end, the best advice possible would be to practice actively going through example case studies, such as those available in the Interview Query questions bank . Exploring different topics with a friend in an interview-like setting with cold recall (no Googling in between!) will be uncomfortable and awkward, but it will also help reveal weaknesses in fleshing out the investigation.

Don’t worry if the first few times are terrible! Developing a rhythm will help with gaining self-confidence as you become better at assessing and learning through these sessions.

Finding the right data science talent for case studies? OutSearch.ai ’s AI-driven platform streamlines this by pinpointing candidates who excel in real-world scenarios. Discover how they can help you match with top problem-solvers.

Product Case Study Questions

With product data science case questions , the interviewer wants to get an idea of your product sense intuition. Specifically, these questions assess your ability to identify which metrics should be proposed in order to understand a product.

1. How would you measure the success of private stories on Instagram, where only certain close friends can see the story?

Start by answering: What is the goal of the private story feature on Instagram? You can’t evaluate “success” without knowing what the initial objective of the product was, to begin with.

One specific goal of this feature would be to drive engagement. A private story could potentially increase interactions between users, and grow awareness of the feature.

Now, what types of metrics might you propose to assess user engagement? For a high-level overview, we could look at:

• Average stories per user per day
• Average Close Friends stories per user per day

However, we would also want to further bucket our users to see the effect that Close Friends stories have on user engagement. By bucketing users by age, date joined, or another metric, we could see how engagement is affected within certain populations, giving us insight on success that could be lost if looking at the overall population.

2. How would you measure the success of acquiring new users through a 30-day free trial at Netflix?

More context: Netflix is offering a promotion where users can enroll in a 30-day free trial. After 30 days, customers will automatically be charged based on their selected package. How would you measure acquisition success, and what metrics would you propose to measure the success of the free trial?

One way we can frame the concept specifically to this problem is to think about controllable inputs, external drivers, and then the observable output . Start with the major goals of Netflix:

• Acquiring new users to their subscription plan.
• Decreasing churn and increasing retention.

Looking at acquisition output metrics specifically, there are several top-level stats that we can look at, including:

• Conversion rate percentage
• Cost per free trial acquisition
• Daily conversion rate

With these conversion metrics, we would also want to bucket users by cohort. This would help us see the percentage of free users who were acquired, as well as retention by cohort.

3. How would you measure the success of Facebook Groups?

Start by considering the key function of Facebook Groups . You could say that Groups are a way for users to connect with other users through a shared interest or real-life relationship. Therefore, the user’s goal is to experience a sense of community, which will also drive our business goal of increasing user engagement.

What general engagement metrics can we associate with this value? An objective metric like Groups monthly active users would help us see if Facebook Groups user base is increasing or decreasing. Plus, we could monitor metrics like posting, commenting, and sharing rates.

There are other products that Groups impact, however, specifically the Newsfeed. We need to consider Newsfeed quality and examine if updates from Groups clog up the content pipeline and if users prioritize those updates over other Newsfeed items. This evaluation will give us a better sense of if Groups actually contribute to higher engagement levels.

4. How would you analyze the effectiveness of a new LinkedIn chat feature that shows a “green dot” for active users?

Note: Given engineering constraints, the new feature is impossible to A/B test before release. When you approach case study questions, remember always to clarify any vague terms. In this case, “effectiveness” is very vague. To help you define that term, you would want first to consider what the goal is of adding a green dot to LinkedIn chat.

5. How would you diagnose why weekly active users are up 5%, but email notification open rates are down 2%?

What assumptions can you make about the relationship between weekly active users and email open rates? With a case question like this, you would want to first answer that line of inquiry before proceeding.

Hint: Open rate can decrease when its numerator decreases (fewer people open emails) or its denominator increases (more emails are sent overall). Taking these two factors into account, what are some hypotheses we can make about our decrease in the open rate compared to our increase in weekly active users?

Data Analytics Case Study Questions

Data analytics case studies ask you to dive into analytics problems. Typically these questions ask you to examine metrics trade-offs or investigate changes in metrics. In addition to proposing metrics, you also have to write SQL queries to generate the metrics, which is why they are sometimes referred to as SQL case study questions .

6. Using the provided data, generate some specific recommendations on how DoorDash can improve.

In this DoorDash analytics case study take-home question you are provided with the following dataset:

• Customer order time
• Restaurant order time
• Driver arrives at restaurant time
• Order delivered time
• Customer ID
• Amount of discount
• Amount of tip

With a dataset like this, there are numerous recommendations you can make. A good place to start is by thinking about the DoorDash marketplace, which includes drivers, riders and merchants. How could you analyze the data to increase revenue, driver/user retention and engagement in that marketplace?

7. After implementing a notification change, the total number of unsubscribes increases. Write a SQL query to show how unsubscribes are affecting login rates over time.

This is a Twitter data science interview question , and let’s say you implemented this new feature using an A/B test. You are provided with two tables: events (which includes login, nologin and unsubscribe ) and variants (which includes control or variant ).

We are tasked with comparing multiple different variables at play here. There is the new notification system, along with its effect of creating more unsubscribes. We can also see how login rates compare for unsubscribes for each bucket of the A/B test.

Given that we want to measure two different changes, we know we have to use GROUP BY for the two variables: date and bucket variant. What comes next?

8. Write a query to disprove the hypothesis: Data scientists who switch jobs more often end up getting promoted faster.

More context: You are provided with a table of user experiences representing each person’s past work experiences and timelines.

This question requires a bit of creative problem-solving to understand how we can prove or disprove the hypothesis. The hypothesis is that a data scientist that ends up switching jobs more often gets promoted faster.

Therefore, in analyzing this dataset, we can prove this hypothesis by separating the data scientists into specific segments on how often they jump in their careers.

For example, if we looked at the number of job switches for data scientists that have been in their field for five years, we could prove the hypothesis that the number of data science managers increased as the number of career jumps also rose.

• Never switched jobs: 10% are managers
• Switched jobs once: 20% are managers
• Switched jobs twice: 30% are managers
• Switched jobs three times: 40% are managers

9. Write a SQL query to investigate the hypothesis: Click-through rate is dependent on search result rating.

More context: You are given a table with search results on Facebook, which includes query (search term), position (the search position), and rating (human rating from 1 to 5). Each row represents a single search and includes a column has_clicked that represents whether a user clicked or not.

This question requires us to formulaically do two things: create a metric that can analyze a problem that we face and then actually compute that metric.

Think about the data we want to display to prove or disprove the hypothesis. Our output metric is CTR (clickthrough rate). If CTR is high when search result ratings are high and CTR is low when the search result ratings are low, then our hypothesis is proven. However, if the opposite is true, CTR is low when the search result ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

With that structure in mind, we can then look at the results split into different search rating buckets. If we measure the CTR for queries that all have results rated at 1 and then measure CTR for queries that have results rated at lower than 2, etc., we can measure to see if the increase in rating is correlated with an increase in CTR.

10. How would you help a supermarket chain determine which product categories should be prioritized in their inventory restructuring efforts?

You’re working as a Data Scientist in a local grocery chain’s data science team. The business team has decided to allocate store floor space by product category (e.g., electronics, sports and travel, food and beverages). Help the team understand which product categories to prioritize as well as answering questions such as how customer demographics affect sales, and how each city’s sales per product category differs.

Check out our Data Analytics Learning Path .

Modeling and Machine Learning Case Questions

Machine learning case questions assess your ability to build models to solve business problems. These questions can range from applying machine learning to solve a specific case scenario to assessing the validity of a hypothetical existing model . The modeling case study requires a candidate to evaluate and explain any certain part of the model building process.

11. Describe how you would build a model to predict Uber ETAs after a rider requests a ride.

Common machine learning case study problems like this are designed to explain how you would build a model. Many times this can be scoped down to specific parts of the model building process. Examining the example above, we could break it up into:

How would you evaluate the predictions of an Uber ETA model?

What features would you use to predict the Uber ETA for ride requests?

Our recommended framework breaks down a modeling and machine learning case study to individual steps in order to tackle each one thoroughly. In each full modeling case study, you will want to go over:

• Data processing
• Feature Selection
• Model Selection
• Cross Validation
• Evaluation Metrics
• Testing and Roll Out

12. How would you build a model that sends bank customers a text message when fraudulent transactions are detected?

Additionally, the customer can approve or deny the transaction via text response.

Let’s start out by understanding what kind of model would need to be built. We know that since we are working with fraud, there has to be a case where either a fraudulent transaction is or is not present .

Hint: This problem is a binary classification problem. Given the problem scenario, what considerations do we have to think about when first building this model? What would the bank fraud data look like?

13. How would you design the inputs and outputs for a model that detects potential bombs at a border crossing?

Additional questions. How would you test the model and measure its accuracy? Remember the equation for precision:

Because we can not have high TrueNegatives, recall should be high when assessing the model.

14. Which model would you choose to predict Airbnb booking prices: Linear regression or random forest regression?

Start by answering this question: What are the main differences between linear regression and random forest?

Random forest regression is based on the ensemble machine learning technique of bagging . The two key concepts of random forests are:

• Random sampling of training observations when building trees.
• Random subsets of features for splitting nodes.

Random forest regressions also discretize continuous variables, since they are based on decision trees and can split categorical and continuous variables.

Linear regression, on the other hand, is the standard regression technique in which relationships are modeled using a linear predictor function, the most common example represented as y = Ax + B.

Let’s see how each model is applicable to Airbnb’s bookings. One thing we need to do in the interview is to understand more context around the problem of predicting bookings. To do so, we need to understand which features are present in our dataset.

We can assume the dataset will have features like:

• Location features.
• Seasonality.
• Number of bedrooms and bathrooms.
• Private room, shared, entire home, etc.
• External demand (conferences, festivals, sporting events).

Which model would be the best fit for this feature set?

15. Using a binary classification model that pre-approves candidates for a loan, how would you give each rejected application a rejection reason?

More context: You do not have access to the feature weights. Start by thinking about the problem like this: How would the problem change if we had ten, one thousand, or ten thousand applicants that had gone through the loan qualification program?

Pretend that we have three people: Alice, Bob, and Candace that have all applied for a loan. Simplifying the financial lending loan model, let us assume the only features are the total number of credit cards , the dollar amount of current debt , and credit age . Here is a scenario:

Alice: 10 credit cards, 5 years of credit age, $\$20K$ in debt

Bob: 10 credit cards, 5 years of credit age, $\$15K$ in debt

Candace: 10 credit cards, 5 years of credit age, $\$10K$ in debt

If Candace is approved, we can logically point to the fact that Candace’s $\$10K$ in debt swung the model to approve her for a loan. How did we reason this out?

If the sample size analyzed was instead thousands of people who had the same number of credit cards and credit age with varying levels of debt, we could figure out the model’s average loan acceptance rate for each numerical amount of current debt. Then we could plot these on a graph to model the y-value (average loan acceptance) versus the x-value (dollar amount of current debt). These graphs are called partial dependence plots.

Business Case Questions

In data science interviews, business case study questions task you with addressing problems as they relate to the business. You might be asked about topics like estimation and calculation, as well as applying problem-solving to a larger case. One tip: Be sure to read up on the company’s products and ventures before your interview to expose yourself to possible topics.

16. How would you estimate the average lifetime value of customers at a business that has existed for just over one year?

More context: You know that the product costs $\$100$ per month, averages 10% in monthly churn, and the average customer stays for 3.5 months.

Remember that lifetime value is defined by the prediction of the net revenue attributed to the entire future relationship with all customers averaged. Therefore, $\$100$ * 3.5 = $\$350$… But is it that simple?

Because this company is so new, our average customer length (3.5 months) is biased from the short possible length of time that anyone could have been a customer (one year maximum). How would you then model out LTV knowing the churn rate and product cost?

17. How would you go about removing duplicate product names (e.g. iPhone X vs. Apple iPhone 10) in a massive database?

See the full solution for this Amazon business case question on YouTube:

18. What metrics would you monitor to know if a 50% discount promotion is a good idea for a ride-sharing company?

This question has no correct answer and is rather designed to test your reasoning and communication skills related to product/business cases. First, start by stating your assumptions. What are the goals of this promotion? It is likely that the goal of the discount is to grow revenue and increase retention. A few other assumptions you might make include:

• The promotion will be applied uniformly across all users.
• The 50% discount can only be used for a single ride.

How would we be able to evaluate this pricing strategy? An A/B test between the control group (no discount) and test group (discount) would allow us to evaluate Long-term revenue vs average cost of the promotion. Using these two metrics how could we measure if the promotion is a good idea?

19. A bank wants to create a new partner card, e.g. Whole Foods Chase credit card). How would you determine what the next partner card should be?

More context: Say you have access to all customer spending data. With this question, there are several approaches you can take. As your first step, think about the business reason for credit card partnerships: they help increase acquisition and customer retention.

One of the simplest solutions would be to sum all transactions grouped by merchants. This would identify the merchants who see the highest spending amounts. However, the one issue might be that some merchants have a high-spend value but low volume. How could we counteract this potential pitfall? Is the volume of transactions even an important factor in our credit card business? The more questions you ask, the more may spring to mind.

20. How would you assess the value of keeping a TV show on a streaming platform like Netflix?

Say that Netflix is working on a deal to renew the streaming rights for a show like The Office , which has been on Netflix for one year. Your job is to value the benefit of keeping the show on Netflix.

Start by trying to understand the reasons why Netflix would want to renew the show. Netflix mainly has three goals for what their content should help achieve:

• Acquisition: To increase the number of subscribers.
• Retention: To increase the retention of active subscribers and keep them on as paying members.
• Revenue: To increase overall revenue.

One solution to value the benefit would be to estimate a lower and upper bound to understand the percentage of users that would be affected by The Office being removed. You could then run these percentages against your known acquisition and retention rates.

21. How would you determine which products are to be put on sale?

Let’s say you work at Amazon. It’s nearing Black Friday, and you are tasked with determining which products should be put on sale. You have access to historical pricing and purchasing data from items that have been on sale before. How would you determine what products should go on sale to best maximize profit during Black Friday?

To start with this question, aggregate data from previous years for products that have been on sale during Black Friday or similar events. You can then compare elements such as historical sales volume, inventory levels, and profit margins.

Learn More About Feature Changes

This course is designed teach you everything you need to know about feature changes:

More Data Science Interview Resources

Case studies are one of the most common types of data science interview questions . Practice with the data science course from Interview Query, which includes product and machine learning modules.

• Data Science

Top 12 Data Science Case Studies: Across Various Industries

Home Blog Data Science Top 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI . An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey. In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Aspiring and practising data scientists can motivate themselves to learn more about the sector, an alternative way of thinking, or methods to improve their organization based on comparable experiences. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this data science course content . From my standpoint, data scientists may use it to spot fraudulent conduct in insurance claims. Automotive data scientists may use it to improve self-driving cars. In contrast, e-commerce data scientists can use it to add more personalization for their consumers—the possibilities are unlimited and unexplored. Let’s look at the top eight data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more. Read on to explore more or use the following links to go straight to the case study of your choice.

Examples of Data Science Case Studies

• Hospitality:  Airbnb focuses on growth by  analyzing  customer voice using data science.  Qantas uses predictive analytics to mitigate losses  
• Healthcare:  Novo Nordisk  is  Driving innovation with NLP.  AstraZeneca harnesses data for innovation in medicine  
• Covid 19:  Johnson and Johnson use s  d ata science  to fight the Pandemic  
• E-commerce:  Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  
• Supply chain management :  UPS optimizes supp l y chain with big data analytics
• Meteorology:  IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  
• Entertainment Industry:  Netflix  u ses data science to personalize the content and improve recommendations.  Spotify uses big   data to deliver a rich user experience for online music streaming  
• Banking and Finance:  HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience  

Top 8 Data Science Case Studies  [For Various Industries]

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing , tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science.  A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk.  Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine.  AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management , drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

9. Data Science in Urban Planning and Smart Cities  

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.  

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.  

10. Data Science in Agricultural Yield Prediction   

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.  

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.  

11. Data Science in the Transportation Industry   

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.  

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.  

12. Data Science in the Environmental Industry    

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.  

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a swot analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. This approach allows them to differentiate themselves from their competitors and offer something unique to their customers.  

With data science, the companies have become smarter and more data-driven to bring about tremendous growth. Moreover, data science has made these organizations more sustainable. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

• Defining the problem statement and strategy to solve it  
• Gather and pre-process the data by making relevant assumptions  
• Select tool and appropriate algorithms to build machine learning /deep learning models 
• Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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6 of my favorite case studies in Data Science!

Data scientists are numbers people. They have a deep understanding of statistics and algorithms, programming and hacking, and communication skills. Data science is about applying these three skill sets in a disciplined and systematic manner, with the goal of improving an aspect of the business. That’s the data science process . In order to stay abreast of industry trends, data scientists often turn to case studies. Reviewing these is a helpful way for both aspiring and working data scientists to challenge themselves and learn more about a particular field, a different way of thinking, or ways to better their own company based on similar experiences. If you’re not familiar with case studies , they’ve been described as “an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables.” Data science is used by pretty much every industry out there. Insurance claims analysts can use data science to identify fraudulent behavior, e-commerce data scientists can build personalized experiences for their customers, music streaming companies can use it to create different genres of playlists—the possibilities are endless. Allow us to share a few of our favorite data science case studies with you so you can see first hand how companies across a variety of industries leveraged big data to drive productivity, profits, and more.

6 case studies in Data Science

• How Airbnb characterizes data science
• How data science is involved in decision-making at Airbnb
• How Airbnb has scaled its data science efforts across all aspects of the company

Airbnb says that “we’re at a point where our infrastructure is stable, our tools are sophisticated, and our warehouse is clean and reliable. We’re ready to take on exciting new problems.” 3. Spotify’s “This Is” Playlists: The Ultimate Song Analysis For 50 Mainstream Artists If you’re a music lover, you’ve probably used Spotify at least once. If you’re a regular user, you’ve likely taken note of their personalized playlists and been impressed at how well the songs catered to your music preferences. But have you ever thought about how Spotify categorizes their music? You can thank their data science teams for that. The goal of the “This Is” case study is to analyze the music of various Spotify artists, segment the styles, and categorize them into by loudness, danceability, energy, and more. To start, a data scientist looked at Spotify’s API, which collects and provides data from Spotify’s music catalog. Once the data researcher accessed the data from Spotify’s API, he:

• Processed the data to extract audio features for each artist
• Visualized the data using D3.js.
• Applied k-means clustering to separate the artists into different groups
• Analyzed each feature for all the artists

Want a sneak peek at the results? James Arthur and Post Malone are in the same cluster, Kendrick Lamar is the “fastest” artist, and Marshmello beat Martin Garrix in the energy category. 4. A Leading Online Travel Agency Increases Revenues by 16 Percent with Actionable Analytics One of the largest online travel agencies in the world generated the majority of its revenue through its website and directed most of its resources there, but its clients were still using offline channels such as faxes and phone calls to ask questions. The agency brought in WNS, a travel-focused business process management company, to help it determine how to rethink and redesign its roadmap to capture missed revenue opportunities. WNS determined that the agency lacked an adequate offline strategy, which resulted in a dip in revenue and market share. After a deep dive into customer segments, the performance of offline sales agents, ideal hours for sales agents, and more, WNS was able to help the agency increase offline revenue by 16 percent and increase conversion rates by 21 percent. 5. How Mint.com Grew from Zero to 1 Million Users Mint.com is a free personal finance management service that asks users to input their personal spending data to generate insights about where their money goes. When Noah Kagan joined Mint.com as its marketing director, his goal was to find 100,000 new members in just six months. He didn’t just meet that goal. He destroyed it, generating one million members. How did he do it? Kagan says his success was two-fold. This first part was having a product he believed in. The second he attributes to “reverse engineering marketing.” “The key focal point to this strategy is to work backward,” Kagan explained. “Instead of starting with an intimidating zero playing on your mind, start at the solution and map your plan back from there.” He went on: “Think of it as a road trip. You start with a set destination in mind and then plan your route there. You don’t get in your car and start driving without in the hope that you magically end up where you wanted to be.” 6. Netflix: Using Big Data to Drive Big Engagement One of the best ways to explain the benefits of data science to people who don’t quite grasp the industry is by using Netflix-focused examples. Yes, Netflix is the largest internet-television network in the world. But what most people don’t realize is that, at its core, Netflix is a customer-focused, data-driven business. Founded in 1997 as a mail-order DVD company, it now boasts more than 53 million members in approximately 50 countries. If you watch The Fast and The Furious on Friday night, Netflix will likely serve up a Mark Wahlberg movie among your personalized recommendations for Saturday night. This is due to data science. But did you know that the company also uses its data insights to inform the way it buys, licenses, and creates new content? House of Cards and Orange is the New Black are two examples of how the company leveraged big data to understand its subscribers and cater to their needs. The company’s most-watched shows are generated from recommendations, which in turn foster consumer engagement and loyalty. This is why the company is constantly working on its recommendation engines. The Netflix story is a perfect case study for those who require engaged audiences in order to survive. In summary, data scientists are companies’ secret weapons when it comes to understanding customer behavior and levering it to drive conversion, loyalty, and profits. These six data science case studies show you how a variety of organizations—from a nature conservation group to a finance company to a media company—leveraged their big data to not only survive but to beat out the competition.

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Case studies

Notes for contributors

Case studies are a core feature of the Real World Data Science platform. Our case studies are designed to show how data science is used to solve real-world problems in business, public policy and beyond.

A good case study will be a source of information, insight and inspiration for each of our target audiences:

• Practitioners will learn from their peers – whether by seeing new techniques applied to common problems, or familiar techniques adapted to unique challenges.
• Leaders will see how different data science teams work, the mix of skills and experience in play, and how the components of the data science process fit together.
• Students will enrich their understanding of how data science is applied, how data scientists operate, and what skills they need to hone to succeed in the workplace.

Case studies should follow the structure below. It is not necessary to use the section headings we have provided – creativity and variety are encouraged. However, the areas outlined under each section heading should be covered in all submissions.

• The problem/challenge Summarise the project and its relevance to your organisation’s needs, aims and ambitions.
• Goals Specify what exactly you sought to achieve with this project.
• Background An opportunity to explain more about your organisation, your team’s work leading up to this project, and to introduce audiences more generally to the type of problem/challenge you faced, particularly if it is a problem/challenge that may be experienced by organisations working in different sectors and industries.
• Approach Describe how you turned the organisational problem/challenge into a task that could be addressed by data science. Explain how you proposed to tackle the problem, including an introduction, explanation and (possibly) a demonstration of the method, model or algorithm used. (NB: If you have a particular interest and expertise in the method, model or algorithm employed, including the history and development of the approach, please consider writing an Explainer article for us.) Discuss the pros and cons, strengths and limitations of the approach.
• Implementation Walk audiences through the implementation process. Discuss any challenges you faced, the ethical questions you needed to ask and answer, and how you tested the approach to ensure that outcomes would be robust, unbiased, good quality, and aligned with the goals you set out to achieve.
• Impact How successful was the project? Did you achieve your goals? How has the project benefited your organisation? How has the project benefited your team? Does it inform or pave the way for future projects?
• Learnings What are your key takeaways from the project? Are there lessons that you can apply to future projects, or are there learnings for other data scientists working on similar problems/challenges?

Advice and recommendations

You do not need to divulge the detailed inner workings of your organisation. Audiences are mostly interested in understanding the general use case and the problem-solving process you went through, to see how they might apply the same approach within their own organisations.

Goals can be defined quite broadly. There’s no expectation that you set out your organisation’s short- or long-term targets. Instead, audiences need to know enough about what you want to do so they can understand what motivates your choice of approach.

Use toy examples and synthetic data to good effect. We understand that – whether for commercial, legal or ethical reasons – it can be difficult or impossible to share real data in your case studies, or to describe the actual outputs of your work. However, there are many ways to share learnings and insights without divulging sensitive information. This blog post from Lyft uses hypotheticals, mathematical notation and synthetic data to explain the company’s approach to causal forecasting without revealing actual KPIs or data.

People like to experiment, so encourage them to do so. Our platform allows you to embed code and to link that code to interactive coding environments like Google Colab . So if, for example, you want to explain a technique like bootstrapping , why not provide a code block so that audiences can run a bootstrapping simulation themselves.

Leverage links. You can’t be expected to explain or cover every detail in one case study, so feel free to point audiences to other sources of information that can enrich their understanding: blogs, videos, journal articles, conference papers, etc.

Data science case interviews (what to expect & how to prepare)

Data science case studies are tough to crack: they’re open-ended, technical, and specific to the company. Interviewers use them to test your ability to break down complex problems and your use of analytical thinking to address business concerns.

So we’ve put together this guide to help you familiarize yourself with case studies at companies like Amazon, Google, and Meta (Facebook), as well as how to prepare for them, using practice questions and a repeatable answer framework.

Here’s the first thing you need to know about tackling data science case studies: always start by asking clarifying questions, before jumping in to your plan.

Let’s get started.

• What to expect in data science case study interviews
• How to approach data science case studies
• Sample cases from FAANG data science interviews
• How to prepare for data science case interviews

Click here to practice 1-on-1 with ex-FAANG interviewers

1. what to expect in data science case study interviews.

Before we get into an answer method and practice questions for data science case studies, let’s take a look at what you can expect in this type of interview.

Of course, the exact interview process for data scientist candidates will depend on the company you’re applying to, but case studies generally appear in both the pre-onsite phone screens and during the final onsite or virtual loop.

These questions may take anywhere from 10 to 40 minutes to answer, depending on the depth and complexity that the interviewer is looking for. During the initial phone screens, the case studies are typically shorter and interspersed with other technical and/or behavioral questions. During the final rounds, they will likely take longer to answer and require a more detailed analysis.

While some candidates may have the opportunity to prepare in advance and present their conclusions during an interview round, most candidates work with the information the interviewer offers on the spot.

1.1 The types of data science case studies

Generally, there are two types of case studies:

• Analysis cases , which focus on how you translate user behavior into ideas and insights using data. These typically center around a product, feature, or business concern that’s unique to the company you’re interviewing with.
• Modeling cases , which are more overtly technical and focus on how you build and use machine learning and statistical models to address business problems.

The number of case studies that you’ll receive in each category will depend on the company and the position that you’ve applied for. Facebook , for instance, typically doesn’t give many machine learning modeling cases, whereas Amazon does.

Also, some companies break these larger groups into smaller subcategories. For example, Facebook divides its analysis cases into two types: product interpretation and applied data . 

You may also receive in-depth questions similar to case studies, which test your technical capabilities (e.g. coding, SQL), so if you’d like to learn more about how to answer coding interview questions, take a look here .

We’ll give you a step-by-step method that can be used to answer analysis and modeling cases in section 2 . But first, let’s look at how interviewers will assess your answers.

1.2 What interviewers are looking for

We’ve researched accounts from ex-interviewers and data scientists to pinpoint the main criteria that interviewers look for in your answers. While the exact grading rubric will vary per company, this list from an ex-Google data scientist is a good overview of the biggest assessment areas:

• Structure : candidate can break down an ambiguous problem into clear steps
• Completeness : candidate is able to fully answer the question
• Soundness : candidate’s solution is feasible and logical
• Clarity : candidate’s explanations and methodology are easy to understand
• Speed : candidate manages time well and is able to come up with solutions quickly

You’ll be able to improve your skills in each of these categories by practicing data science case studies on your own, and by working with an answer framework. We’ll get into that next.

2. How to approach data science case studies

Approaching data science cases with a repeatable framework will not only add structure to your answer, but also help you manage your time and think clearly under the stress of interview conditions.

Let’s go over a framework that you can use in your interviews, then break it down with an example answer.

2.1 Data science case framework: CAPER

We've researched popular frameworks used by real data scientists, and consolidated them to be as memorable and useful in an interview setting as possible.

Try using the framework below to structure your thinking during the interview. 

• Clarify : Start by asking questions. Case questions are ambiguous, so you’ll need to gather more information from the interviewer, while eliminating irrelevant data. The types of questions you’ll ask will depend on the case, but consider: what is the business objective? What data can I access? Should I focus on all customers or just in X region?
• Assume : Narrow the problem down by making assumptions and stating them to the interviewer for confirmation. (E.g. the statistical significance is X%, users are segmented based on XYZ, etc.) By the end of this step you should have constrained the problem into a clear goal.
• Plan : Now, begin to craft your solution. Take time to outline a plan, breaking it into manageable tasks. Once you’ve made your plan, explain each step that you will take to the interviewer, and ask if it sounds good to them.
• Execute : Carry out your plan, walking through each step with the interviewer. Depending on the type of case, you may have to prepare and engineer data, code, apply statistical algorithms, build a model, etc. In the majority of cases, you will need to end with business analysis.
• Review : Finally, tie your final solution back to the business objectives you and the interviewer had initially identified. Evaluate your solution, and whether there are any steps you could have added or removed to improve it. 

Now that you’ve seen the framework, let’s take a look at how to implement it.

2.2 Sample answer using the CAPER framework

Below you’ll find an answer to a Facebook data science interview question from the Applied Data loop. This is an example that comes from Facebook’s data science interview prep materials, which you can find here .

Try this question:

Imagine that Facebook is building a product around high schools, starting with about 300 million users who have filled out a field with the name of their current high school. How would you find out how much of this data is real?

First, we need to clarify the question, eliminating irrelevant data and pinpointing what is the most important. For example:

• What exactly does “real” mean in this context?
• Should we focus on whether the high school itself is real, or whether the user actually attended the high school they’ve named?

After discussing with the interviewer, we’ve decided to focus on whether the high school itself is real first, followed by whether the user actually attended the high school they’ve named.

Next, we’ll narrow the problem down and state our assumptions to the interviewer for confirmation. Here are some assumptions we could make in the context of this problem:

• The 300 million users are likely teenagers, given that they’re listing their current high school
• We can assume that a high school that is listed too few times is likely fake
• We can assume that a high school that is listed too many times (e.g. 10,000+ students) is likely fake

The interviewer has agreed with each of these assumptions, so we can now move on to the plan.

Next, it’s time to make a list of actionable steps and lay them out for the interviewer before moving on.

First, there are two approaches that we can identify:

• A high precision approach, which provides a list of people who definitely went to a confirmed high school
• A high recall approach, more similar to market sizing, which would provide a ballpark figure of people who went to a confirmed high school

As this is for a product that Facebook is currently building, the product use case likely calls for an estimate that is as accurate as possible. So we can go for the first approach, which will provide a more precise estimate of confirmed users listing a real high school. 

Now, we list the steps that make up this approach:

• To find whether a high school is real: Draw a distribution with the number of students on the X axis, and the number of high schools on the Y axis, in order to find and eliminate the lower and upper bounds
• To find whether a student really went to a high school: use a user’s friend graph and location to determine the plausibility of the high school they’ve named

The interviewer has approved the plan, which means that it’s time to execute.

4. Execute 

Step 1: Determining whether a high school is real

Going off of our plan, we’ll first start with the distribution.

We can use x1 to denote the lower bound, below which the number of times a high school is listed would be too small for a plausible school. x2 then denotes the upper bound, above which the high school has been listed too many times for a plausible school.

Here is what that would look like:

Be prepared to answer follow up questions. In this case, the interviewer may ask, “looking at this graph, what do you think x1 and x2 would be?”

Based on this distribution, we could say that x1 is approximately the 5th percentile, or somewhere around 100 students. So, out of 300 million students, if fewer than 100 students list “Applebee” high school, then this is most likely not a real high school.

x2 is likely around the 95th percentile, or potentially as high as the 99th percentile. Based on intuition, we could estimate that number around 10,000. So, if more than 10,000 students list “Applebee” high school, then this is most likely not real. Here is how that looks on the distribution:

At this point, the interviewer may ask more follow-up questions, such as “how do we account for different high schools that share the same name?”

In this case, we could group by the schools’ name and location, rather than name alone. If the high school does not have a dedicated page that lists its location, we could deduce its location based on the city of the user that lists it. 

Step 2: Determining whether a user went to the high school

A strong signal as to whether a user attended a specific high school would be their friend graph: a set number of friends would have to have listed the same current high school. For now, we’ll set that number at five friends.

Don’t forget to call out trade-offs and edge cases as you go. In this case, there could be a student who has recently moved, and so the high school they’ve listed does not reflect their actual current high school. 

To solve this, we could rely on users to update their location to reflect the change. If users do not update their location and high school, this would present an edge case that we would need to work out later.

To conclude, we could use the data from both the friend graph and the initial distribution to confirm the two signifiers: a high school is real, and the user really went there.

If enough users in the same location list the same high school, then it is likely that the high school is real, and that the users really attend it. If there are not enough users in the same location that list the same high school, then it is likely that the high school is not real, and the users do not actually attend it.

3. Sample cases from FAANG data science interviews

Having worked through the sample problem above, try out the different kinds of case studies that have been asked in data science interviews at FAANG companies. We’ve divided the questions into types of cases, as well as by company.

For more information about each of these companies’ data science interviews, take a look at these guides:

• Facebook data scientist interview guide
• Amazon data scientist interview guide
• Google data scientist interview guide

Now let’s get into the questions. This is a selection of real data scientist interview questions, according to data from Glassdoor.

Data science case studies

Facebook - Analysis (product interpretation)

• How would you measure the success of a product?
• What KPIs would you use to measure the success of the newsfeed?
• Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?

Facebook - Analysis (applied data)

• How would you evaluate the impact for teenagers when their parents join Facebook?
• How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
• How would you set up an experiment to understand feature change in Instagram stories?

Amazon - modeling

• How would you improve a classification model that suffers from low precision?
• When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?

Google - Analysis

• You have a google app and you make a change. How do you test if a metric has increased or not?
• How do you detect viruses or inappropriate content on YouTube?
• How would you compare if upgrading the android system produces more searches?

4. How to prepare for data science case interviews

Understanding the process and learning a method for data science cases will go a long way in helping you prepare. But this information is not enough to land you a data science job offer. 

To succeed in your data scientist case interviews, you're also going to need to practice under realistic interview conditions so that you'll be ready to perform when it counts. 

For more information on how to prepare for data science interviews as a whole, take a look at our guide on data science interview prep .

4.1 Practice on your own

Start by answering practice questions alone. You can use the list in section 3 , and interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. 

Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. This will help you get used to the answer framework and get used to answering data science cases in a structured way.

4.2 Practice with peers

Once you’re used to answering questions on your own , then a great next step is to do mock interviews with friends or peers. This will help you adapt your approach to accommodate for follow-ups and answer questions you haven’t already worked through.

This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

4.3 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at a big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today .

Related articles:

Data Science Case Study Interview: Your Guide to Success

by Sam McKay, CFA | Careers

Ready to crush your next data science interview? Well, you’re in the right place.

This type of interview is designed to assess your problem-solving skills, technical knowledge, and ability to apply data-driven solutions to real-world challenges.

So, how can you master these interviews and secure your next job?

To master your data science case study interview:

Practice Case Studies: Engage in mock scenarios to sharpen problem-solving skills.

Review Core Concepts: Brush up on algorithms, statistical analysis, and key programming languages.

Contextualize Solutions: Connect findings to business objectives for meaningful insights.

Clear Communication: Present results logically and effectively using visuals and simple language.

Adaptability and Clarity: Stay flexible and articulate your thought process during problem-solving.

This article will delve into each of these points and give you additional tips and practice questions to get you ready to crush your upcoming interview!

After you’ve read this article, you can enter the interview ready to showcase your expertise and win your dream role.

Let’s dive in!

Table of Contents

What to Expect in the Interview?

Data science case study interviews are an essential part of the hiring process. They give interviewers a glimpse of how you, approach real-world business problems and demonstrate your analytical thinking, problem-solving, and technical skills.

Furthermore, case study interviews are typically open-ended , which means you’ll be presented with a problem that doesn’t have a right or wrong answer.

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

This type of interview allows your potential employer to evaluate your creativity, technical knowledge, and attention to detail.

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

In a case study interview , you can expect inquiries that cover a spectrum of topics crucial to evaluating your skill set:

Topic 1: Problem-Solving Scenarios

In these interviews, your ability to resolve genuine business dilemmas using data-driven methods is essential.

These scenarios reflect authentic challenges, demanding analytical insight, decision-making, and problem-solving skills.

Real-world Challenges: Expect scenarios like optimizing marketing strategies, predicting customer behavior, or enhancing operational efficiency through data-driven solutions.

Analytical Thinking: Demonstrate your capacity to break down complex problems systematically, extracting actionable insights from intricate issues.

Decision-making Skills: Showcase your ability to make informed decisions, emphasizing instances where your data-driven choices optimized processes or led to strategic recommendations.

Your adeptness at leveraging data for insights, analytical thinking, and informed decision-making defines your capability to provide practical solutions in real-world business contexts.

Topic 2: Data Handling and Analysis

Data science case studies assess your proficiency in data preprocessing, cleaning, and deriving insights from raw data.

Data Collection and Manipulation: Prepare for data engineering questions involving data collection, handling missing values, cleaning inaccuracies, and transforming data for analysis.

Handling Missing Values and Cleaning Data: Showcase your skills in managing missing values and ensuring data quality through cleaning techniques.

Data Transformation and Feature Engineering: Highlight your expertise in transforming raw data into usable formats and creating meaningful features for analysis.

Mastering data preprocessing—managing, cleaning, and transforming raw data—is fundamental. Your proficiency in these techniques showcases your ability to derive valuable insights essential for data-driven solutions.

Topic 3: Modeling and Feature Selection

Data science case interviews prioritize your understanding of modeling and feature selection strategies.

Model Selection and Application: Highlight your prowess in choosing appropriate models, explaining your rationale, and showcasing implementation skills.

Feature Selection Techniques: Understand the importance of selecting relevant variables and methods, such as correlation coefficients, to enhance model accuracy.

Ensuring Robustness through Random Sampling: Consider techniques like random sampling to bolster model robustness and generalization abilities.

Excel in modeling and feature selection by understanding contexts, optimizing model performance, and employing robust evaluation strategies.

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

These interviews require proficiency in statistical and machine learning methods for diverse problem-solving. This topic is significant for anyone applying for a machine learning engineer position.

Using Statistical Models: Utilize logistic and linear regression models for effective classification and prediction tasks.

Leveraging Machine Learning Algorithms: Employ models such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees for complex pattern recognition and classification.

Exploring Deep Learning Techniques: Consider neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) for intricate data patterns.

Experimentation and Model Selection: Experiment with various algorithms to identify the most suitable approach for specific contexts.

Combining statistical and machine learning expertise equips you to systematically tackle varied data challenges, ensuring readiness for case studies and beyond.

Topic 5: Evaluation Metrics and Validation

In data science interviews, understanding evaluation metrics and validation techniques is critical to measuring how well machine learning models perform.

Choosing the Right Metrics: Select metrics like precision, recall (for classification), or R² (for regression) based on the problem type. Picking the right metric defines how you interpret your model’s performance.

Validating Model Accuracy: Use methods like cross-validation and holdout validation to test your model across different data portions. These methods prevent errors from overfitting and provide a more accurate performance measure.

Importance of Statistical Significance: Evaluate if your model’s performance is due to actual prediction or random chance. Techniques like hypothesis testing and confidence intervals help determine this probability accurately.

Interpreting Results: Be ready to explain model outcomes, spot patterns, and suggest actions based on your analysis. Translating data insights into actionable strategies showcases your skill.

Finally, focusing on suitable metrics, using validation methods, understanding statistical significance, and deriving actionable insights from data underline your ability to evaluate model performance.

Also, being well-versed in these topics and having hands-on experience through practice scenarios can significantly enhance your performance in these case study interviews.

Prepare to demonstrate technical expertise and adaptability, problem-solving, and communication skills to excel in these assessments.

Now, let’s talk about how to navigate the interview.

Here is a step-by-step guide to get you through the process.

Steps by Step Guide Through the Interview

This section’ll discuss what you can expect during the interview process and how to approach case study questions.

Step 1: Problem Statement: You’ll be presented with a problem or scenario—either a hypothetical situation or a real-world challenge—emphasizing the need for data-driven solutions within data science.

Step 2: Clarification and Context: Seek more profound clarity by actively engaging with the interviewer. Ask pertinent questions to thoroughly understand the objectives, constraints, and nuanced aspects of the problem statement.

Step 3: State your Assumptions: When crucial information is lacking, make reasonable assumptions to proceed with your final solution. Explain these assumptions to your interviewer to ensure transparency in your decision-making process.

Step 4: Gather Context: Consider the broader business landscape surrounding the problem. Factor in external influences such as market trends, customer behaviors, or competitor actions that might impact your solution.

Step 5: Data Exploration: Delve into the provided datasets meticulously. Cleanse, visualize, and analyze the data to derive meaningful and actionable insights crucial for problem-solving.

Step 6: Modeling and Analysis: Leverage statistical or machine learning techniques to address the problem effectively. Implement suitable models to derive insights and solutions aligning with the identified objectives.

Step 7: Results Interpretation: Interpret your findings thoughtfully. Identify patterns, trends, or correlations within the data and present clear, data-backed recommendations relevant to the problem statement.

Step 8: Results Presentation: Effectively articulate your approach, methodologies, and choices coherently. This step is vital, especially when conveying complex technical concepts to non-technical stakeholders.

Remember to remain adaptable and flexible throughout the process and be prepared to adapt your approach to each situation.

Now that you have a guide on navigating the interview, let us give you some tips to help you stand out from the crowd.

Top 3 Tips to Master Your Data Science Case Study Interview

Approaching case study interviews in data science requires a blend of technical proficiency and a holistic understanding of business implications.

Here are practical strategies and structured approaches to prepare effectively for these interviews:

1. Comprehensive Preparation Tips

To excel in case study interviews, a blend of technical competence and strategic preparation is key.

Here are concise yet powerful tips to equip yourself for success:

Practice with Mock Case Studies : Familiarize yourself with the process through practice. Online resources offer example questions and solutions, enhancing familiarity and boosting confidence.

Review Your Data Science Toolbox: Ensure a strong foundation in fundamentals like data wrangling, visualization, and machine learning algorithms. Comfort with relevant programming languages is essential.

Simplicity in Problem-solving: Opt for clear and straightforward problem-solving approaches. While advanced techniques can be impressive, interviewers value efficiency and clarity.

Interviewers also highly value someone with great communication skills. Here are some tips to highlight your skills in this area.

2. Communication and Presentation of Results

In case study interviews, communication is vital. Present your findings in a clear, engaging way that connects with the business context. Tips include:

Contextualize results: Relate findings to the initial problem, highlighting key insights for business strategy.

Use visuals: Charts, graphs, or diagrams help convey findings more effectively.

Logical sequence: Structure your presentation for easy understanding, starting with an overview and progressing to specifics.

Simplify ideas: Break down complex concepts into simpler segments using examples or analogies.

Mastering these techniques helps you communicate insights clearly and confidently, setting you apart in interviews.

Lastly here are some preparation strategies to employ before you walk into the interview room.

3. Structured Preparation Strategy

Prepare meticulously for data science case study interviews by following a structured strategy.

Here’s how:

Practice Regularly: Engage in mock interviews and case studies to enhance critical thinking and familiarity with the interview process. This builds confidence and sharpens problem-solving skills under pressure.

Thorough Review of Concepts: Revisit essential data science concepts and tools, focusing on machine learning algorithms, statistical analysis, and relevant programming languages (Python, R, SQL) for confident handling of technical questions.

Strategic Planning: Develop a structured framework for approaching case study problems. Outline the steps and tools/techniques to deploy, ensuring an organized and systematic interview approach.

Understanding the Context: Analyze business scenarios to identify objectives, variables, and data sources essential for insightful analysis.

Ask for Clarification: Engage with interviewers to clarify any unclear aspects of the case study questions. For example, you may ask ‘What is the business objective?’ This exhibits thoughtfulness and aids in better understanding the problem.

Transparent Problem-solving: Clearly communicate your thought process and reasoning during problem-solving. This showcases analytical skills and approaches to data-driven solutions.

Blend technical skills with business context, communicate clearly, and prepare to systematically ace your case study interviews.

Now, let’s really make this specific.

Each company is different and may need slightly different skills and specializations from data scientists.

However, here is some of what you can expect in a case study interview with some industry giants.

Case Interviews at Top Tech Companies

As you prepare for data science interviews, it’s essential to be aware of the case study interview format utilized by top tech companies.

In this section, we’ll explore case interviews at Facebook, Twitter, and Amazon, and provide insight into what they expect from their data scientists.

Facebook predominantly looks for candidates with strong analytical and problem-solving skills. The case study interviews here usually revolve around assessing the impact of a new feature, analyzing monthly active users, or measuring the effectiveness of a product change.

To excel during a Facebook case interview, you should break down complex problems, formulate a structured approach, and communicate your thought process clearly.

Twitter , similar to Facebook, evaluates your ability to analyze and interpret large datasets to solve business problems. During a Twitter case study interview, you might be asked to analyze user engagement, develop recommendations for increasing ad revenue, or identify trends in user growth.

Be prepared to work with different analytics tools and showcase your knowledge of relevant statistical concepts.

Amazon is known for its customer-centric approach and data-driven decision-making. In Amazon’s case interviews, you may be tasked with optimizing customer experience, analyzing sales trends, or improving the efficiency of a certain process.

Keep in mind Amazon’s leadership principles, especially “Customer Obsession” and “Dive Deep,” as you navigate through the case study.

Remember, practice is key. Familiarize yourself with various case study scenarios and hone your data science skills.

With all this knowledge, it’s time to practice with the following practice questions.

Mockup Case Studies and Practice Questions

To better prepare for your data science case study interviews, it’s important to practice with some mockup case studies and questions.

One way to practice is by finding typical case study questions.

Here are a few examples to help you get started:

Customer Segmentation: You have access to a dataset containing customer information, such as demographics and purchase behavior. Your task is to segment the customers into groups that share similar characteristics. How would you approach this problem, and what machine-learning techniques would you consider?

Fraud Detection: Imagine your company processes online transactions. You are asked to develop a model that can identify potentially fraudulent activities. How would you approach the problem and which features would you consider using to build your model? What are the trade-offs between false positives and false negatives?

Demand Forecasting: Your company needs to predict future demand for a particular product. What factors should be taken into account, and how would you build a model to forecast demand? How can you ensure that your model remains up-to-date and accurate as new data becomes available?

By practicing case study interview questions , you can sharpen problem-solving skills, and walk into future data science interviews more confidently.

Remember to practice consistently and stay up-to-date with relevant industry trends and techniques.

Final Thoughts

Data science case study interviews are more than just technical assessments; they’re opportunities to showcase your problem-solving skills and practical knowledge.

Furthermore, these interviews demand a blend of technical expertise, clear communication, and adaptability.

Remember, understanding the problem, exploring insights, and presenting coherent potential solutions are key.

By honing these skills, you can demonstrate your capability to solve real-world challenges using data-driven approaches. Good luck on your data science journey!

Frequently Asked Questions

How would you approach identifying and solving a specific business problem using data.

To identify and solve a business problem using data, you should start by clearly defining the problem and identifying the key metrics that will be used to evaluate success.

Next, gather relevant data from various sources and clean, preprocess, and transform it for analysis. Explore the data using descriptive statistics, visualizations, and exploratory data analysis.

Based on your understanding, build appropriate models or algorithms to address the problem, and then evaluate their performance using appropriate metrics. Iterate and refine your models as necessary, and finally, communicate your findings effectively to stakeholders.

Can you describe a time when you used data to make recommendations for optimization or improvement?

Recall a specific data-driven project you have worked on that led to optimization or improvement recommendations. Explain the problem you were trying to solve, the data you used for analysis, the methods and techniques you employed, and the conclusions you drew.

Share the results and how your recommendations were implemented, describing the impact it had on the targeted area of the business.

How would you deal with missing or inconsistent data during a case study?

When dealing with missing or inconsistent data, start by assessing the extent and nature of the problem. Consider applying imputation methods, such as mean, median, or mode imputation, or more advanced techniques like k-NN imputation or regression-based imputation, depending on the type of data and the pattern of missingness.

For inconsistent data, diagnose the issues by checking for typos, duplicates, or erroneous entries, and take appropriate corrective measures. Document your handling process so that stakeholders can understand your approach and the limitations it might impose on the analysis.

What techniques would you use to validate the results and accuracy of your analysis?

To validate the results and accuracy of your analysis, use techniques like cross-validation or bootstrapping, which can help gauge model performance on unseen data. Employ metrics relevant to your specific problem, such as accuracy, precision, recall, F1-score, or RMSE, to measure performance.

Additionally, validate your findings by conducting sensitivity analyses, sanity checks, and comparing results with existing benchmarks or domain knowledge.

How would you communicate your findings to both technical and non-technical stakeholders?

To effectively communicate your findings to technical stakeholders, focus on the methodology, algorithms, performance metrics, and potential improvements. For non-technical stakeholders, simplify complex concepts and explain the relevance of your findings, the impact on the business, and actionable insights in plain language.

Use visual aids, like charts and graphs, to illustrate your results and highlight key takeaways. Tailor your communication style to the audience, and be prepared to answer questions and address concerns that may arise.

How do you choose between different machine learning models to solve a particular problem?

When choosing between different machine learning models, first assess the nature of the problem and the data available to identify suitable candidate models. Evaluate models based on their performance, interpretability, complexity, and scalability, using relevant metrics and techniques such as cross-validation, AIC, BIC, or learning curves.

Consider the trade-offs between model accuracy, interpretability, and computation time, and choose a model that best aligns with the problem requirements, project constraints, and stakeholders’ expectations.

Keep in mind that it’s often beneficial to try several models and ensemble methods to see which one performs best for the specific problem at hand.

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• Data Science
• Data Analytics
• Machine Learning

Essential Statistics for Data Science: A Case Study using Python, Part I

Get to know some of the essential statistics you should be very familiar with when learning data science

LearnDataSci is reader-supported. When you purchase through links on our site, earned commissions help support our team of writers, researchers, and designers at no extra cost to you.

Our last post dove straight into linear regression. In this post, we'll take a step back to cover essential statistics that every data scientist should know. To demonstrate these essentials, we'll look at a hypothetical case study involving an administrator tasked with improving school performance in Tennessee.

You should already know:

• Python fundamentals — learn on dataquest.io

Note, this tutorial is intended to serve solely as an educational tool and not as a scientific explanation of the causes of various school outcomes in Tennessee .

Article Resources

• Notebook and Data: Github
• Libraries: pandas, matplotlib, seaborn

Introduction

Meet Sally, a public school administrator. Some schools in her state of Tennessee are performing below average academically. Her superintendent, under pressure from frustrated parents and voters, approached Sally with the task of understanding why these schools are under-performing. Not an easy problem, to be sure.

To improve school performance, Sally needs to learn more about these schools and their students, just as a business needs to understand its own strengths and weaknesses and its customers.

Though Sally is eager to build an impressive explanatory model, she knows the importance of conducting preliminary research to prevent possible pitfalls or blind spots (e.g. cognitive bias'). Thus, she engages in a thorough exploratory analysis, which includes: a lit review, data collection, descriptive and inferential statistics, and data visualization.

Sally has strong opinions as to why some schools are under-performing, but opinions won't do, nor will a handful of facts; she needs rigorous statistical evidence.

Sally conducts a lit review, which involves reading a variety of credible sources to familiarize herself with the topic. Most importantly, Sally keeps an open mind and embraces a scientific world view to help her resist confirmation bias (seeking solely to confirm one's own world view).

In Sally's lit review, she finds multiple compelling explanations of school performance: curriculae , income , and parental involvement . These sources will help Sally select her model and data, and will guide her interpretation of the results.

Data Collection

The data we want isn't always available, but Sally lucks out and finds student performance data based on test scores ( school_rating ) for every public school in middle Tennessee. The data also includes various demographic, school faculty, and income variables (see readme for more information). Satisfied with this dataset, she writes a web-scraper to retrieve the data.

But data alone can't help Sally; she needs to convert the data into useful information.

Descriptive and Inferential Statistics

Sally opens her stats textbook and finds that there are two major types of statistics, descriptive and inferential.

Descriptive statistics identify patterns in the data, but they don't allow for making hypotheses about the data.

Within descriptive statistics, there are two measures used to describe the data: central tendency and deviation . Central tendency refers to the central position of the data (mean, median, mode) while the deviation describes how far spread out the data are from the mean. Deviation is most commonly measured with the standard deviation. A small standard deviation indicates the data are close to the mean, while a large standard deviation indicates that the data are more spread out from the mean.

Inferential statistics allow us to make hypotheses (or inferences ) about a sample that can be applied to the population. For Sally, this involves developing a hypothesis about her sample of middle Tennessee schools and applying it to her population of all schools in Tennessee.

For now, Sally puts aside inferential statistics and digs into descriptive statistics.

To begin learning about the sample, Sally uses pandas' describe method, as seen below. The column headers in bold text represent the variables Sally will be exploring. Each row header represents a descriptive statistic about the corresponding column.

Looking at the output above, Sally's variables can be put into two classes: measurements and indicators.

Measurements are variables that can be quantified. All data in the output above are measurements. Some of these measurements, such as state_percentile_16 , avg_score_16 and school_rating , are outcomes; these outcomes cannot be used to explain one another. For example, explaining school_rating as a result of state_percentile_16 (test scores) is circular logic. Therefore we need a second class of variables.

The second class, indicators, are used to explain our outcomes. Sally chooses indicators that describe the student body (for example, reduced_lunch ) or school administration ( stu_teach_ratio ) hoping they will explain school_rating .

Sally sees a pattern in one of the indicators, reduced_lunch . reduced_lunch is a variable measuring the average percentage of students per school enrolled in a federal program that provides lunches for students from lower-income households. In short, reduced_lunch is a good proxy for household income, which Sally remembers from her lit review was correlated with school performance.

Sally isolates reduced_lunch and groups the data by school_rating using pandas' groupby method and then uses describe on the re-shaped data (see below).

Below is a discussion of the metrics from the table above and what each result indicates about the relationship between school_rating and reduced_lunch :

count : the number of schools at each rating. Most of the schools in Sally's sample have a 4- or 5-star rating, but 25% of schools have a 1-star rating or below. This confirms that poor school performance isn't merely anecdotal, but a serious problem that deserves attention.

mean : the average percentage of students on reduced_lunch among all schools by each school_rating . As school performance increases, the average number of students on reduced lunch decreases. Schools with a 0-star rating have 83.6% of students on reduced lunch. And on the other end of the spectrum, 5-star schools on average have 21.6% of students on reduced lunch. We'll examine this pattern further. in the graphing section.

std : the standard deviation of the variable. Referring to the school_rating of 0, a standard deviation of 8.813498 indicates that 68.2% (refer to readme ) of all observations are within 8.81 percentage points on either side of the average, 83.6%. Note that the standard deviation increases as school_rating increases, indicating that reduced_lunch loses explanatory power as school performance improves. As with the mean, we'll explore this idea further in the graphing section.

min : the minimum value of the variable. This represents the school with the lowest percentage of students on reduced lunch at each school rating. For 0- and 1-star schools, the minimum percentage of students on reduced lunch is 53%. The minimum for 5-star schools is 2%. The minimum value tells a similar story as the mean, but looking at it from the low end of the range of observations.

25% : the bottom quartile; represents the lowest 25% of values for the variable, reduced_lunch . For 0-star schools, 25% of the observations are less than 79.5%. Sally sees the same trend in the bottom quartile as the above metrics: as school_rating increases the bottom 25% of reduced_lunch decreases.

50% : the second quartile; represents the lowest 50% of values. Looking at the trend in school_rating and reduced_lunch , the same relationship is present here.

75% : the top quartile; represents the lowest 75% of values. The trend continues.

max : the maximum value for that variable. You guessed it: the trend continues!

The descriptive statistics consistently reveal that schools with more students on reduced lunch under-perform when compared to their peers. Sally is on to something.

Sally decides to look at reduced_lunch from another angle using a correlation matrix with pandas' corr method. The values in the correlation matrix table will be between -1 and 1 (see below). A value of -1 indicates the strongest possible negative correlation, meaning as one variable decreases the other increases. And a value of 1 indicates the opposite. The result below, -0.815757, indicates strong negative correlation between reduced_lunch and school_rating . There's clearly a relationship between the two variables.

Sally continues to explore this relationship graphically.

Essential Graphs for Exploring Data

Box-and-whisker plot.

In her stats book, Sally sees a box-and-whisker plot . A box-and-whisker plot is helpful for visualizing the distribution of the data from the mean. Understanding the distribution allows Sally to understand how far spread out her data is from the mean; the larger the spread from the mean, the less robust reduced_lunch is at explaining school_rating .

See below for an explanation of the box-and-whisker plot.

Now that Sally knows how to read the box-and-whisker plot, she graphs reduced_lunch to see the distributions. See below.

In her box-and-whisker plots, Sally sees that the minimum and maximum reduced_lunch values tend to get closer to the mean as school_rating decreases; that is, as school_rating decreases so does the standard deviation in reduced_lunch .

What does this mean?

Starting with the top box-and-whisker plot, as school_rating decreases, reduced_lunch becomes a more powerful way to explain outcomes. This could be because as parents' incomes decrease they have fewer resources to devote to their children's education (such as, after-school programs, tutors, time spent on homework, computer camps, etc) than higher-income parents. Above a 3-star rating, more predictors are needed to explain school_rating due to an increasing spread in reduced_lunch .

Having used box-and-whisker plots to reaffirm her idea that household income and school performance are related, Sally seeks further validation.

Scatter Plot

To further examine the relationship between school_rating and reduced_lunch , Sally graphs the two variables on a scatter plot. See below.

In the scatter plot above, each dot represents a school. The placement of the dot represents that school's rating (Y-axis) and the percentage of its students on reduced lunch (x-axis).

The downward trend line shows the negative correlation between school_rating and reduced_lunch (as one increases, the other decreases). The slope of the trend line indicates how much school_rating decreases as reduced_lunch increases. A steeper slope would indicate that a small change in reduced_lunch has a big impact on school_rating while a more horizontal slope would indicate that the same small change in reduced_lunch has a smaller impact on school_rating .

Sally notices that the scatter plot further supports what she saw with the box-and-whisker plot: when reduced_lunch increases, school_rating decreases. The tighter spread of the data as school_rating declines indicates the increasing influence of reduced_lunch . Now she has a hypothesis.

Correlation Matrix

Sally is ready to test her hypothesis: a negative relationship exists between school_rating and reduced_lunch (to be covered in a follow up article). If the test is successful, she'll need to build a more robust model using additional variables. If the test fails, she'll need to re-visit her dataset to choose other variables that possibly explain school_rating . Either way, Sally could benefit from an efficient way of assessing relationships among her variables.

An efficient graph for assessing relationships is the correlation matrix, as seen below; its color-coded cells make it easier to interpret than the tabular correlation matrix above. Red cells indicate positive correlation; blue cells indicate negative correlation; white cells indicate no correlation. The darker the colors, the stronger the correlation (positive or negative) between those two variables.

With the correlation matrix in mind as a future starting point for finding additional variables, Sally moves on for now and prepares to test her hypothesis.

Sally was approached with a problem: why are some schools in middle Tennessee under-performing? To answer this question, she did the following:

• Conducted a lit review to educate herself on the topic.
• Gathered data from a reputable source to explore school ratings and characteristics of the student bodies and schools in middle Tennessee.
• The data indicated a robust relationship between school_rating and reduced_lunch .
• Explored the data visually.
• Though satisfied with her preliminary findings, Sally is keeping her mind open to other explanations.
• Developed a hypothesis: a negative relationship exists between school_rating and reduced_lunch .

In a follow up article, Sally will test her hypothesis. Should she find a satisfactory explanation for her sample of schools, she will attempt to apply her explanation to the population of schools in Tennessee.

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• Data Science Interview Questions

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Introduction:

Data science is an interdisciplinary field that mines raw data, analyses it, and comes up with patterns that are used to extract valuable insights from it. Statistics, computer science, machine learning, deep learning, data analysis, data visualization, and various other technologies form the core foundation of data science.

Over the years, data science has gained widespread importance due to the importance of data. Data is considered the new oil of the future which when analyzed and harnessed properly can prove to be very beneficial to the stakeholders. Not just this, a data scientist gets exposure to work in diverse domains, solving real-life practical problems all by making use of trendy technologies. The most common real-time application is fast delivery of food in apps such as Uber Eats by aiding the delivery person to show the fastest possible route to reach the destination from the restaurant. 

Data Science is also used in item recommendation systems in e-commerce sites like Amazon, Flipkart, etc which recommend the user what item they can buy based on their search history. Not just recommendation systems, Data Science is becoming increasingly popular in fraud detection applications to detect any fraud involved in credit-based financial applications. A successful data scientist can interpret data, perform innovation and bring out creativity while solving problems that help drive business and strategic goals. This makes it the most lucrative job of the 21st century.

In this article, we will explore what are the most commonly asked Data Science Technical Interview Questions which will help both aspiring and experienced data scientists.

Data Science Interview Questions for Freshers

Data science interview questions for experienced, frequently asked questions, data science mcq, 1. what is data science.

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science.

The following figure represents the life cycle of data science.

• It starts with gathering the business requirements and relevant data.
• Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture.
• Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.
• Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements.
• In the final stage, the results are communicated to the business in a visually appealing manner. This is where the skill of data visualization, reporting, and different business intelligence tools come into the picture. Learn More .

2. What is the difference between data analytics and data science?

• Data science involves the task of transforming data by using various technical analysis methods to extract meaningful insights using which a data analyst can apply to their business scenarios.
• Data analytics deals with checking the existing hypothesis and information and answers questions for a better and effective business-related decision-making process.
• Data Science drives innovation by answering questions that build connections and answers for futuristic problems. Data analytics focuses on getting present meaning from existing historical context whereas data science focuses on predictive modeling.
• Data Science can be considered as a broad subject that makes use of various mathematical and scientific tools and algorithms for solving complex problems whereas data analytics can be considered as a specific field dealing with specific concentrated problems using fewer tools of statistics and visualization.

The following Venn diagram depicts the difference between data science and data analytics clearly:

3. What are some of the techniques used for sampling? What is the main advantage of sampling?

Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. It becomes crucial to take some data samples that can be used for representing the whole population and then perform analysis on it. While doing this, it is very much necessary to carefully take sample data out of the huge data that truly represents the entire dataset.

There are majorly two categories of sampling techniques based on the usage of statistics, they are:

• Probability Sampling techniques: Clustered sampling, Simple random sampling, Stratified sampling.
• Non-Probability Sampling techniques: Quota sampling, Convenience sampling, snowball sampling, etc.

4. List down the conditions for Overfitting and Underfitting.

Overfitting: The model performs well only for the sample training data. If any new data is given as input to the model, it fails to provide any result. These conditions occur due to low bias and high variance in the model. Decision trees are more prone to overfitting.

Underfitting: Here, the model is so simple that it is not able to identify the correct relationship in the data, and hence it does not perform well even on the test data. This can happen due to high bias and low variance. Linear regression is more prone to Underfitting.

5. Differentiate between the long and wide format data.

The following image depicts the representation of wide format and long format data:

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6. what are eigenvectors and eigenvalues.

Eigenvectors are column vectors or unit vectors whose length/magnitude is equal to 1. They are also called right vectors. Eigenvalues are coefficients that are applied on eigenvectors which give these vectors different values for length or magnitude.

A matrix can be decomposed into Eigenvectors and Eigenvalues and this process is called Eigen decomposition. These are then eventually used in machine learning methods like PCA (Principal Component Analysis) for gathering valuable insights from the given matrix.

7. What does it mean when the p-values are high and low?

A p-value is the measure of the probability of having results equal to or more than the results achieved under a specific hypothesis assuming that the null hypothesis is correct. This represents the probability that the observed difference occurred randomly by chance.

• Low p-value which means values ≤ 0.05 means that the null hypothesis can be rejected and the data is unlikely with true null.
• High p-value, i.e values ≥ 0.05 indicates the strength in favor of the null hypothesis. It means that the data is like with true null.
• p-value = 0.05 means that the hypothesis can go either way.

8. When is resampling done?

Resampling is a methodology used to sample data for improving accuracy and quantify the uncertainty of population parameters. It is done to ensure the model is good enough by training the model on different patterns of a dataset to ensure variations are handled. It is also done in the cases where models need to be validated using random subsets or when substituting labels on data points while performing tests.

9. What do you understand by Imbalanced Data?

Data is said to be highly imbalanced if it is distributed unequally across different categories. These datasets result in an error in model performance and result in inaccuracy.

10. Are there any differences between the expected value and mean value?

There are not many differences between these two, but it is to be noted that these are used in different contexts. The mean value generally refers to the probability distribution whereas the expected value is referred to in the contexts involving random variables.

11. What do you understand by Survivorship Bias?

This bias refers to the logical error while focusing on aspects that survived some process and overlooking those that did not work due to lack of prominence. This bias can lead to deriving wrong conclusions.

12. Define the terms KPI, lift, model fitting, robustness and DOE.

• KPI: KPI stands for Key Performance Indicator that measures how well the business achieves its objectives.
• Lift: This is a performance measure of the target model measured against a random choice model. Lift indicates how good the model is at prediction versus if there was no model.
• Model fitting: This indicates how well the model under consideration fits given observations.
• Robustness: This represents the system’s capability to handle differences and variances effectively.
• DOE: stands for the design of experiments, which represents the task design aiming to describe and explain information variation under hypothesized conditions to reflect variables.

13. Define confounding variables.

Confounding variables are also known as confounders. These variables are a type of extraneous variables that influence both independent and dependent variables causing spurious association and mathematical relationships between those variables that are associated but are not casually related to each other.

14. Define and explain selection bias?

The selection bias occurs in the case when the researcher has to make a decision on which participant to study. The selection bias is associated with those researches when the participant selection is not random. The selection bias is also called the selection effect. The selection bias is caused by as a result of the method of sample collection.

Four types of selection bias are explained below:

• Sampling Bias: As a result of a population that is not random at all, some members of a population have fewer chances of getting included than others, resulting in a biased sample. This causes a systematic error known as sampling bias.
• Time interval: Trials may be stopped early if we reach any extreme value but if all variables are similar invariance, the variables with the highest variance have a higher chance of achieving the extreme value.
• Data: It is when specific data is selected arbitrarily and the generally agreed criteria are not followed.
• Attrition: Attrition in this context means the loss of the participants. It is the discounting of those subjects that did not complete the trial.

15. Define bias-variance trade-off?

Let us first understand the meaning of bias and variance in detail:

Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. When a model is trained, at that time it makes simplified assumptions so that it can easily understand the target function. Some algorithms that have low bias are Decision Trees, SVM, etc. On the other hand, logistic and linear regression algorithms are the ones with a high bias.

Variance: Variance is also a kind of error. It is introduced into an ML Model when an ML algorithm is made highly complex. This model also learns noise from the data set that is meant for training. It further performs badly on the test data set. This may lead to over lifting as well as high sensitivity.

When the complexity of a model is increased, a reduction in the error is seen. This is caused by the lower bias in the model. But, this does not happen always till we reach a particular point called the optimal point. After this point, if we keep on increasing the complexity of the model, it will be over lifted and will suffer from the problem of high variance. We can represent this situation with the help of a graph as shown below:

As you can see from the image above, before the optimal point, increasing the complexity of the model reduces the error (bias). However, after the optimal point, we see that the increase in the complexity of the machine learning model increases the variance.

Trade-off Of Bias And Variance: So, as we know that bias and variance, both are errors in machine learning models, it is very essential that any machine learning model has low variance as well as a low bias so that it can achieve good performance.

Let us see some examples. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance. This trade-off can easily be reversed by increasing the k value which in turn results in increasing the number of neighbours. This, in turn, results in increasing the bias and reducing the variance.

Another example can be the algorithm of a support vector machine. This algorithm also has a high variance and obviously, a low bias and we can reverse the trade-off by increasing the value of parameter C. Thus, increasing the C parameter increases the bias and decreases the variance.

So, the trade-off is simple. If we increase the bias, the variance will decrease and vice versa.

16. Define the confusion matrix?

It is a matrix that has 2 rows and 2 columns. It has 4 outputs that a binary classifier provides to it. It is used to derive various measures like specificity, error rate, accuracy, precision, sensitivity, and recall.

The test data set should contain the correct and predicted labels. The labels depend upon the performance. For instance, the predicted labels are the same if the binary classifier performs perfectly. Also, they match the part of observed labels in real-world scenarios. The four outcomes shown above in the confusion matrix mean the following:

• True Positive: This means that the positive prediction is correct.
• False Positive: This means that the positive prediction is incorrect.
• True Negative: This means that the negative prediction is correct.
• False Negative: This means that the negative prediction is incorrect.

The formulas for calculating basic measures that comes from the confusion matrix are:

• Error rate : (FP + FN)/(P + N)
• Accuracy : (TP + TN)/(P + N)
• Sensitivity = TP/P
• Specificity = TN/N
• Precision = TP/(TP + FP)
• F-Score  = (1 + b)(PREC.REC)/(b2 PREC + REC) Here, b is mostly 0.5 or 1 or 2.

In these formulas:

FP = false positive FN = false negative TP = true positive RN = true negative

Sensitivity is the measure of the True Positive Rate. It is also called recall. Specificity is the measure of the true negative rate. Precision is the measure of a positive predicted value. F-score is the harmonic mean of precision and recall.

17. What is logistic regression? State an example where you have recently used logistic regression.

Logistic Regression is also known as the logit model. It is a technique to predict the binary outcome from a linear combination of variables (called the predictor variables). 

For example , let us say that we want to predict the outcome of elections for a particular political leader. So, we want to find out whether this leader is going to win the election or not. So, the result is binary i.e. win (1) or loss (0). However, the input is a combination of linear variables like the money spent on advertising, the past work done by the leader and the party, etc. 

18. What is Linear Regression? What are some of the major drawbacks of the linear model?

Linear regression is a technique in which the score of a variable Y is predicted using the score of a predictor variable X. Y is called the criterion variable. Some of the drawbacks of Linear Regression are as follows:

• The assumption of linearity of errors is a major drawback.
• It cannot be used for binary outcomes. We have Logistic Regression for that.
• Overfitting problems are there that can’t be solved.

19. What is a random forest? Explain it’s working.

Classification is very important in machine learning. It is very important to know to which class does an observation belongs. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. One such classification technique that is near the top of the classification hierarchy is the random forest classifier. 

So, firstly we need to understand a decision tree before we can understand the random forest classifier and its works. So, let us say that we have a string as given below:

So, we have the string with 5 ones and 4 zeroes and we want to classify the characters of this string using their features. These features are colour (red or green in this case) and whether the observation (i.e. character) is underlined or not. Now, let us say that we are only interested in red and underlined observations. So, the decision tree would look something like this:

So, we started with the colour first as we are only interested in the red observations and we separated the red and the green-coloured characters. After that, the “No” branch i.e. the branch that had all the green coloured characters was not expanded further as we want only red-underlined characters. So, we expanded the “Yes” branch and we again got a “Yes” and a “No” branch based on the fact whether the characters were underlined or not. 

So, this is how we draw a typical decision tree. However, the data in real life is not this clean but this was just to give an idea about the working of the decision trees. Let us now move to the random forest.

Random Forest

It consists of a large number of decision trees that operate as an ensemble. Basically, each tree in the forest gives a class prediction and the one with the maximum number of votes becomes the prediction of our model. For instance, in the example shown below, 4 decision trees predict 1, and 2 predict 0. Hence, prediction 1 will be considered.

The underlying principle of a random forest is that several weak learners combine to form a keen learner. The steps to build a random forest are as follows:

• Build several decision trees on the samples of data and record their predictions.
• Each time a split is considered for a tree, choose a random sample of mm predictors as the split candidates out of all the pp predictors. This happens to every tree in the random forest.
• Apply the rule of thumb i.e. at each split m = p√m = p.
• Apply the predictions to the majority rule.

20. In a time interval of 15-minutes, the probability that you may see a shooting star or a bunch of them is 0.2. What is the percentage chance of you seeing at least one star shooting from the sky if you are under it for about an hour?

Let us say that Prob is the probability that we may see a minimum of one shooting star in 15 minutes.

So, Prob = 0.2

Now, the probability that we may not see any shooting star in the time duration of 15 minutes is = 1 - Prob

1-0.2 = 0.8

The probability that we may not see any shooting star for an hour is: 

= (1-Prob)(1-Prob)(1-Prob)*(1-Prob) = 0.8 * 0.8 * 0.8 * 0.8 = (0.8)⁴   ≈ 0.40

So, the probability that we will see one shooting star in the time interval of an hour is = 1-0.4 = 0.6

So, there are approximately 60% chances that we may see a shooting star in the time span of an hour.

21. What is deep learning? What is the difference between deep learning and machine learning?

Deep learning is a paradigm of machine learning. In deep learning,  multiple layers of processing are involved in order to extract high features from the data. The neural networks are designed in such a way that they try to simulate the human brain. 

Deep learning has shown incredible performance in recent years because of the fact that it shows great analogy with the human brain.

The difference between machine learning and deep learning is that deep learning is a paradigm or a part of machine learning that is inspired by the structure and functions of the human brain called the artificial neural networks. Learn More .

22. What is a Gradient and Gradient Descent?

Gradient: Gradient is the measure of a property that how much the output has changed with respect to a little change in the input. In other words, we can say that it is a measure of change in the weights with respect to the change in error. The gradient can be mathematically represented as the slope of a function.

Gradient Descent: Gradient descent is a minimization algorithm that minimizes the Activation function. Well, it can minimize any function given to it but it is usually provided with the activation function only. 

Gradient descent, as the name suggests means descent or a decrease in something. The analogy of gradient descent is often taken as a person climbing down a hill/mountain. The following is the equation describing what gradient descent means:

So, if a person is climbing down the hill, the next position that the climber has to come to is denoted by “b” in this equation. Then, there is a minus sign because it denotes the minimization (as gradient descent is a minimization algorithm). The Gamma is called a waiting factor and the remaining term which is the Gradient term itself shows the direction of the steepest descent. 

This situation can be represented in a graph as follows:

Here, we are somewhere at the “Initial Weights” and we want to reach the Global minimum. So, this minimization algorithm will help us do that.

1. How are the time series problems different from other regression problems?

• Time series data can be thought of as an extension to linear regression which uses terms like autocorrelation, movement of averages for summarizing historical data of y-axis variables for predicting a better future.
• Forecasting and prediction is the main goal of time series problems where accurate predictions can be made but sometimes the underlying reasons might not be known.
• Having Time in the problem does not necessarily mean it becomes a time series problem. There should be a relationship between target and time for a problem to become a time series problem.
• The observations close to one another in time are expected to be similar to the ones far away which provide accountability for seasonality. For instance, today’s weather would be similar to tomorrow’s weather but not similar to weather from 4 months from today. Hence, weather prediction based on past data becomes a time series problem.

2. What are RMSE and MSE in a linear regression model?

RMSE: RMSE stands for Root Mean Square Error. In a linear regression model, RMSE is used to test the performance of the machine learning model. It is used to evaluate the data spread around the line of best fit. So, in simple words, it is used to measure the deviation of the residuals.

RMSE is calculated using the formula:

• Yi is the actual value of the output variable.
• Y(Cap) is the predicted value and,
• N is the number of data points.

MSE: Mean Squared Error is used to find how close is the line to the actual data. So, we make the difference in the distance of the data points from the line and the difference is squared. This is done for all the data points and the submission of the squared difference divided by the total number of data points gives us the Mean Squared Error (MSE).

So, if we are taking the squared difference of N data points and dividing the sum by N, what does it mean? Yes, it represents the average of the squared difference of a data point from the line i.e. the average of the squared difference between the actual and the predicted values. The formula for finding MSE is given below:

• Yi is the actual value of the output variable (the ith data point)
• Y(cap) is the predicted value and,
• N is the total number of data points.

So, RMSE is the square root of MSE .

3. What are Support Vectors in SVM (Support Vector Machine)?

In the above diagram, we can see that the thin lines mark the distance from the classifier to the closest data points (darkened data points). These are called support vectors. So, we can define the support vectors as the data points or vectors that are nearest (closest) to the hyperplane. They affect the position of the hyperplane. Since they support the hyperplane, they are known as support vectors.

4. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let’s say your laptop’s RAM is only 4GB and you want to train your model on 10GB data set.

What will you do have you experienced such an issue before.

In such types of questions, we first need to ask what ML model we have to train. After that, it depends on whether we have to train a model based on Neural Networks or SVM.

The steps for Neural Networks are given below:

• The Numpy array can be used to load the entire data. It will never store the entire data, rather just create a mapping of the data.
• Now, in order to get some desired data, pass the index into the NumPy Array.
• This data can be used to pass as an input to the neural network maintaining a small batch size.

The steps for SVM are given below:

• For SVM, small data sets can be obtained. This can be done by dividing the big data set.
• The subset of the data set can be obtained as an input if using the partial fit function.
• Repeat the step of using the partial fit method for other subsets as well.

Now, you may describe the situation if you have faced such an issue in your projects or working in machine learning/ data science.

5. Explain Neural Network Fundamentals.

In the human brain, different neurons are present. These neurons combine and perform various tasks. The Neural Network in deep learning tries to imitate human brain neurons. The neural network learns the patterns from the data and uses the knowledge that it gains from various patterns to predict the output for new data, without any human assistance.

A perceptron is the simplest neural network that contains a single neuron that performs 2 functions. The first function is to perform the weighted sum of all the inputs and the second is an activation function.

There are some other neural networks that are more complicated. Such networks consist of the following three layers:

• Input Layer: The neural network has the input layer to receive the input.
• Hidden Layer: There can be multiple hidden layers between the input layer and the output layer. The initially hidden layers are used for detecting the low-level patterns whereas the further layers are responsible for combining output from previous layers to find more patterns.
• Output Layer: This layer outputs the prediction.

An example neural network image is shown below:

6. What is Generative Adversarial Network?

This approach can be understood with the famous example of the wine seller. Let us say that there is a wine seller who has his own shop. This wine seller purchases wine from the dealers who sell him the wine at a low cost so that he can sell the wine at a high cost to the customers. Now, let us say that the dealers whom he is purchasing the wine from, are selling him fake wine. They do this as the fake wine costs way less than the original wine and the fake and the real wine are indistinguishable to a normal consumer (customer in this case). The shop owner has some friends who are wine experts and he sends his wine to them every time before keeping the stock for sale in his shop. So, his friends, the wine experts, give him feedback that the wine is probably fake. Since the wine seller has been purchasing the wine for a long time from the same dealers, he wants to make sure that their feedback is right before he complains to the dealers about it. Now, let us say that the dealers also have got a tip from somewhere that the wine seller is suspicious of them.

So, in this situation, the dealers will try their best to sell the fake wine whereas the wine seller will try his best to identify the fake wine. Let us see this with the help of a diagram shown below:

From the image above, it is clear that a noise vector is entering the generator (dealer) and he generates the fake wine and the discriminator has to distinguish between the fake wine and real wine. This is a Generative Adversarial Network (GAN).

In a GAN, there are 2 main components viz. Generator and Discrminator. So, the generator is a CNN that keeps producing images and the discriminator tries to identify the real images from the fake ones. 

7. What is a computational graph?

A computational graph is also known as a “Dataflow Graph”. Everything in the famous deep learning library TensorFlow is based on the computational graph. The computational graph in Tensorflow has a network of nodes where each node operates. The nodes of this graph represent operations and the edges represent tensors.

8. What are auto-encoders?

Auto-encoders are learning networks. They transform inputs into outputs with minimum possible errors. So, basically, this means that the output that we want should be almost equal to or as close as to input as follows. 

Multiple layers are added between the input and the output layer and the layers that are in between the input and the output layer are smaller than the input layer. It received unlabelled input. This input is encoded to reconstruct the input later.

9. What are Exploding Gradients and Vanishing Gradients?

• Exploding Gradients: Let us say that you are training an RNN. Say, you saw exponentially growing error gradients that accumulate, and as a result of this, very large updates are made to the neural network model weights. These exponentially growing error gradients that update the neural network weights to a great extent are called Exploding Gradients .
• Vanishing Gradients: Let us say again, that you are training an RNN. Say, the slope became too small. This problem of the slope becoming too small is called Vanishing Gradient . It causes a major increase in the training time and causes poor performance and extremely low accuracy.

10. What is the p-value and what does it indicate in the Null Hypothesis?

P-value is a number that ranges from 0 to 1. In a hypothesis test in statistics, the p-value helps in telling us how strong the results are. The claim that is kept for experiment or trial is called Null Hypothesis.

• A low p-value i.e. p-value less than or equal to 0.05 indicates the strength of the results against the Null Hypothesis which in turn means that the Null Hypothesis can be rejected. 
• A high p-value i.e. p-value greater than 0.05 indicates the strength of the results in favour of the Null Hypothesis i.e. for the Null Hypothesis which in turn means that the Null Hypothesis can be accepted.

11. Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning?

Tensorflow is a very famous library in deep learning. The reason is pretty simple actually. It provides C++ as well as Python APIs which makes it very easier to work on. Also, TensorFlow has a fast compilation speed as compared to Keras and Torch (other famous deep learning libraries). Apart from that, Tenserflow supports both GPU and CPU computing devices. Hence, it is a major success and a very popular library for deep learning.

12. Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?

Depending on the size of the dataset, we follow the below ways:

• In case the datasets are small, the missing values are substituted with the mean or average of the remaining data. In pandas, this can be done by using mean = df.mean() where df represents the pandas dataframe representing the dataset and mean() calculates the mean of the data. To substitute the missing values with the calculated mean, we can use df.fillna(mean) .
• For larger datasets, the rows with missing values can be removed and the remaining data can be used for data prediction.

13. What is Cross-Validation?

Cross-Validation is a Statistical technique used for improving a model’s performance. Here, the model will be trained and tested with rotation using different samples of the training dataset to ensure that the model performs well for unknown data. The training data will be split into various groups and the model is run and validated against these groups in rotation.

The most commonly used techniques are:

• K- Fold method
• Leave p-out method
• Leave-one-out method
• Holdout method

14. What are the differences between correlation and covariance?

Although these two terms are used for establishing a relationship and dependency between any two random variables, the following are the differences between them:

• Correlation: This technique is used to measure and estimate the quantitative relationship between two variables and is measured in terms of how strong are the variables related.
• Covariance: It represents the extent to which the variables change together in a cycle. This explains the systematic relationship between pair of variables where changes in one affect changes in another variable.

Mathematically, consider 2 random variables, X and Y where the means are represented as  μ X {"detectHand":false}  and  μ Y {"detectHand":false}  respectively and standard deviations are represented by  σ X {"detectHand":false}  and  σ Y {"detectHand":false}  respectively and E represents the expected value operator, then:

• covarianceXY = E[(X- μ X {"detectHand":false} ),(Y- μ Y {"detectHand":false} )]
• correlationXY = E[(X- μ X {"detectHand":false} ),(Y- μ Y {"detectHand":false} )]/( σ X {"detectHand":false} σ Y {"detectHand":false} ) so that

Based on the above formula, we can deduce that the correlation is dimensionless whereas covariance is represented in units that are obtained from the multiplication of units of two variables.

The following image graphically shows the difference between correlation and covariance:

15. How do you approach solving any data analytics based project?

Generally, we follow the below steps:

• The first step is to thoroughly understand the business requirement/problem
• Next, explore the given data and analyze it carefully. If you find any data missing, get the requirements clarified from the business.
• Data cleanup and preparation step is to be performed next which is then used for modelling. Here, the missing values are found and the variables are transformed.
• Run your model against the data, build meaningful visualization and analyze the results to get meaningful insights.
• Release the model implementation, and track the results and performance over a specified period to analyze the usefulness.
• Perform cross-validation of the model.

Check out the list of data analytics projects .

16. How regularly must we update an algorithm in the field of machine learning?

We do not want to update and make changes to an algorithm on a regular basis as an algorithm is a well-defined step procedure to solve any problem and if the steps keep on updating, it cannot be said well defined anymore. Also, this brings in a lot of problems to the systems already implementing the algorithm as it becomes difficult to bring in continuous and regular changes. So, we should update an algorithm only in any of the following cases:

• If you want the model to evolve as data streams through infrastructure, it is fair to make changes to an algorithm and update it accordingly.
• If the underlying data source is changing, it almost becomes necessary to update the algorithm accordingly.
• If there is a case of non-stationarity, we may update the algorithm.
• One of the most important reasons for updating any algorithm is its underperformance and lack of efficiency. So, if an algorithm lacks efficiency or underperforms it should be either replaced by some better algorithm or it must be updated.

17. Why do we need selection bias?

Selection Bias happens in cases where there is no randomization specifically achieved while picking a part of the dataset for analysis. This bias tells that the sample analyzed does not represent the whole population meant to be analyzed.

• For example, in the below image, we can see that the sample that we selected does not entirely represent the whole population that we have. This helps us to question whether we have selected the right data for analysis or not.

18. Why is data cleaning crucial? How do you clean the data?

While running an algorithm on any data, to gather proper insights, it is very much necessary to have correct and clean data that contains only relevant information. Dirty data most often results in poor or incorrect insights and predictions which can have damaging effects.

For example, while launching any big campaign to market a product, if our data analysis tells us to target a product that in reality has no demand and if the campaign is launched, it is bound to fail. This results in a loss of the company’s revenue. This is where the importance of having proper and clean data comes into the picture.

• Data Cleaning of the data coming from different sources helps in data transformation and results in the data where the data scientists can work on.
• Properly cleaned data increases the accuracy of the model and provides very good predictions.
• If the dataset is very large, then it becomes cumbersome to run data on it. The data cleanup step takes a lot of time (around 80% of the time) if the data is huge. It cannot be incorporated with running the model. Hence, cleaning data before running the model, results in increased speed and efficiency of the model.
• Data cleaning helps to identify and fix any structural issues in the data. It also helps in removing any duplicates and helps to maintain the consistency of the data.

The following diagram represents the advantages of data cleaning:

19. What are the available feature selection methods for selecting the right variables for building efficient predictive models?

While using a dataset in data science or machine learning algorithms, it so happens that not all the variables are necessary and useful to build a model. Smarter feature selection methods are required to avoid redundant models to increase the efficiency of our model. Following are the three main methods in feature selection:

• These methods pick up only the intrinsic properties of features that are measured via univariate statistics and not cross-validated performance. They are straightforward and are generally faster and require less computational resources when compared to wrapper methods.
• There are various filter methods such as the Chi-Square test, Fisher’s Score method, Correlation Coefficient, Variance Threshold, Mean Absolute Difference (MAD) method, Dispersion Ratios, etc.

• These methods need some sort of method to search greedily on all possible feature subsets, access their quality by learning and evaluating a classifier with the feature.
• The selection technique is built upon the machine learning algorithm on which the given dataset needs to fit.
• Forward Selection: Here, one feature is tested at a time and new features are added until a good fit is obtained.
• Backward Selection: Here, all the features are tested and the non-fitting ones are eliminated one by one to see while checking which works better.
• Recursive Feature Elimination: The features are recursively checked and evaluated how well they perform.
• These methods are generally computationally intensive and require high-end resources for analysis. But these methods usually lead to better predictive models having higher accuracy than filter methods.

• Embedded methods constitute the advantages of both filter and wrapper methods by including feature interactions while maintaining reasonable computational costs.
• These methods are iterative as they take each model iteration and carefully extract features contributing to most of the training in that iteration.
• Examples of embedded methods: LASSO Regularization (L1), Random Forest Importance.

20. During analysis, how do you treat the missing values?

To identify the extent of missing values, we first have to identify the variables with the missing values. Let us say a pattern is identified. The analyst should now concentrate on them as it could lead to interesting and meaningful insights. However, if there are no patterns identified, we can substitute the missing values with the median or mean values or we can simply ignore the missing values. 

If the variable is categorical, the common strategies for handling missing values include:

• Assigning a New Category: You can assign a new category, such as "Unknown" or "Other," to represent the missing values.
• Mode imputation: You can replace missing values with the mode, which represents the most frequent category in the variable.
• Using a Separate Category: If the missing values carry significant information, you can create a separate category to indicate missing values.

It's important to select an appropriate strategy based on the nature of the data and the potential impact on subsequent analysis or modelling.

If 80% of the values are missing for a particular variable, then we would drop the variable instead of treating the missing values.

21. Will treating categorical variables as continuous variables result in a better predictive model?

Yes! A categorical variable is a variable that can be assigned to two or more categories with no definite category ordering. Ordinal variables are similar to categorical variables with proper and clear ordering defines. So, if the variable is ordinal, then treating the categorical value as a continuous variable will result in better predictive models.

22. How will you treat missing values during data analysis?

The impact of missing values can be known after identifying what type of variables have missing values.

• If the data analyst finds any pattern in these missing values, then there are chances of finding meaningful insights.
• In case of patterns are not found, then these missing values can either be ignored or can be replaced with default values such as mean, minimum, maximum, or median values.
• Assigning a new category: You can assign a new category, such as "Unknown" or "Other," to represent the missing values.
• Using a separate category : If the missing values carry significant information, you can create a separate category to indicate the missing values. It's important to select an appropriate strategy based on the nature of the data and the potential impact on subsequent analysis or modelling.
• If 80% of values are missing, then it depends on the analyst to either replace them with default values or drop the variables.

23. What does the ROC Curve represent and how to create it?

ROC (Receiver Operating Characteristic) curve is a graphical representation of the contrast between false-positive rates and true positive rates at different thresholds. The curve is used as a proxy for a trade-off between sensitivity and specificity.

The ROC curve is created by plotting values of true positive rates (TPR or sensitivity) against false-positive rates (FPR or (1-specificity)) TPR represents the proportion of observations correctly predicted as positive out of overall positive observations. The FPR represents the proportion of observations incorrectly predicted out of overall negative observations. Consider the example of medical testing, the TPR represents the rate at which people are correctly tested positive for a particular disease.

24. What are the differences between univariate, bivariate and multivariate analysis?

Statistical analyses are classified based on the number of variables processed at a given time.

25. What is the difference between the Test set and validation set?

The test set is used to test or evaluate the performance of the trained model. It evaluates the predictive power of the model. The validation set is part of the training set that is used to select parameters for avoiding model overfitting.

26. What do you understand by a kernel trick?

Kernel functions are generalized dot product functions used for the computing dot product of vectors xx and yy in high dimensional feature space. Kernal trick method is used for solving a non-linear problem by using a linear classifier by transforming linearly inseparable data into separable ones in higher dimensions.

27. Differentiate between box plot and histogram.

Box plots and histograms are both visualizations used for showing data distributions for efficient communication of information. Histograms are the bar chart representation of information that represents the frequency of numerical variable values that are useful in estimating probability distribution, variations and outliers. Boxplots are used for communicating different aspects of data distribution where the shape of the distribution is not seen but still the insights can be gathered. These are useful for comparing multiple charts at the same time as they take less space when compared to histograms.

28. How will you balance/correct imbalanced data?

There are different techniques to correct/balance imbalanced data. It can be done by increasing the sample numbers for minority classes. The number of samples can be decreased for those classes with extremely high data points. Following are some approaches followed to balance data:

• Specificity/Precision: Indicates the number of selected instances that are relevant.
• Sensitivity: Indicates the number of relevant instances that are selected.
• F1 score: It represents the harmonic mean of precision and sensitivity.
• MCC (Matthews correlation coefficient): It represents the correlation coefficient between observed and predicted binary classifications.
• AUC (Area Under the Curve): This represents a relation between the true positive rates and false-positive rates.

For example, consider the below graph that illustrates training data:

Here, if we measure the accuracy of the model in terms of getting "0"s, then the accuracy of the model would be very high -> 99.9%, but the model does not guarantee any valuable information. In such cases, we can apply different evaluation metrics as stated above.

• Under-sampling This balances the data by reducing the size of the abundant class and is used when the data quantity is sufficient. By performing this, a new dataset that is balanced can be retrieved and this can be used for further modeling.
• Over-sampling This is used when data quantity is not sufficient. This method balances the dataset by trying to increase the samples size. Instead of getting rid of extra samples, new samples are generated and introduced by employing the methods of repetition, bootstrapping, etc.
• Perform K-fold cross-validation correctly: Cross-Validation needs to be applied properly while using over-sampling. The cross-validation should be done before over-sampling because if it is done later, then it would be like overfitting the model to get a specific result. To avoid this, resampling of data is done repeatedly with different ratios. 

29. What is better - random forest or multiple decision trees?

Random forest is better than multiple decision trees as random forests are much more robust, accurate, and lesser prone to overfitting as it is an ensemble method that ensures multiple weak decision trees learn strongly.

30. Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. Evaluate the probability of finding at least one shooting star in a one-hour duration?

So the probability is 0.8628 = 86.28%

31. Toss the selected coin 10 times from a jar of 1000 coins. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Estimate the probability of getting a head in the next coin toss.

We know that there are two types of coins - fair and double-headed. Hence, there are two possible ways of choosing a coin. The first is to choose a fair coin and the second is to choose a coin having 2 heads.

P(selecting fair coin) = 999/1000 = 0.999 P(selecting double headed coin) = 1/1000 = 0.001

Using Bayes rule,

So, the answer is 0.7531 or 75.3%.

32. What are some examples when false positive has proven important than false negative?

Before citing instances, let us understand what are false positives and false negatives.

• False Positives are those cases that were wrongly identified as an event even if they were not. They are called Type I errors.
• False Negatives are those cases that were wrongly identified as non-events despite being an event. They are called Type II errors.

Some examples where false positives were important than false negatives are:

• In the medical field: Consider that a lab report has predicted cancer to a patient even if he did not have cancer. This is an example of a false positive error. It is dangerous to start chemotherapy for that patient as he doesn’t have cancer as starting chemotherapy would lead to damage of healthy cells and might even actually lead to cancer.
• In the e-commerce field: Suppose a company decides to start a campaign where they give $100 gift vouchers for purchasing $10000 worth of items without any minimum purchase conditions. They assume it would result in at least 20% profit for items sold above $10000. What if the vouchers are given to the customers who haven’t purchased anything but have been mistakenly marked as those who purchased $10000 worth of products. This is the case of false-positive error.

33. Give one example where both false positives and false negatives are important equally?

In Banking fields: Lending loans are the main sources of income to the banks. But if the repayment rate isn’t good, then there is a risk of huge losses instead of any profits. So giving out loans to customers is a gamble as banks can’t risk losing good customers but at the same time, they can’t afford to acquire bad customers. This case is a classic example of equal importance in false positive and false negative scenarios.

34. Is it good to do dimensionality reduction before fitting a Support Vector Model?

If the features number is greater than observations then doing dimensionality reduction improves the SVM (Support Vector Model).

35. What are various assumptions used in linear regression? What would happen if they are violated?

Linear regression is done under the following assumptions:

• The sample data used for modeling represents the entire population.
• There exists a linear relationship between the X-axis variable and the mean of the Y variable.
• The residual variance is the same for any X values. This is called homoscedasticity
• The observations are independent of one another.
• Y is distributed normally for any value of X.

Extreme violations of the above assumptions lead to redundant results. Smaller violations of these result in greater variance or bias of the estimates.

36. How is feature selection performed using the regularization method?

The method of regularization entails the addition of penalties to different parameters in the machine learning model for reducing the freedom of the model to avoid the issue of overfitting. There are various regularization methods available such as linear model regularization, Lasso/L1 regularization, etc. The linear model regularization applies penalty over coefficients that multiplies the predictors. The Lasso/L1 regularization has the feature of shrinking some coefficients to zero, thereby making it eligible to be removed from the model.

37. How do you identify if a coin is biased?

To identify this, we perform a hypothesis test as below: According to the null hypothesis, the coin is unbiased if the probability of head flipping is 50%. According to the alternative hypothesis, the coin is biased and the probability is not equal to 500. Perform the below steps:

• Flip coin 500 times
• Calculate p-value.
• p-value > alpha: Then null hypothesis holds good and the coin is unbiased.
• p-value < alpha: Then the null hypothesis is rejected and the coin is biased.

38. What is the importance of dimensionality reduction?

The process of dimensionality reduction constitutes reducing the number of features in a dataset to avoid overfitting and reduce the variance. There are mostly 4 advantages of this process:

• This reduces the storage space and time for model execution.
• Removes the issue of multi-collinearity thereby improving the parameter interpretation of the ML model.
• Makes it easier for visualizing data when the dimensions are reduced.
• Avoids the curse of increased dimensionality.

39. How is the grid search parameter different from the random search tuning strategy?

Tuning strategies are used to find the right set of hyperparameters. Hyperparameters are those properties that are fixed and model-specific before the model is tested or trained on the dataset. Both the grid search and random search tuning strategies are optimization techniques to find efficient hyperparameters.

• Here, every combination of a preset list of hyperparameters is tried out and evaluated.
• The search pattern is similar to searching in a grid where the values are in a matrix and a search is performed. Each parameter set is tried out and their accuracy is tracked. after every combination is tried out, the model with the highest accuracy is chosen as the best one.
• The main drawback here is that, if the number of hyperparameters is increased, the technique suffers. The number of evaluations can increase exponentially with each increase in the hyperparameter. This is called the problem of dimensionality in a grid search.

• In this technique, random combinations of hyperparameters set are tried and evaluated for finding the best solution. For optimizing the search, the function is tested at random configurations in parameter space as shown in the image below.
• In this method, there are increased chances of finding optimal parameters because the pattern followed is random. There are chances that the model is trained on optimized parameters without the need for aliasing.
• This search works the best when there is a lower number of dimensions as it takes less time to find the right set.

Conclusion:

Data Science is a very vast field and comprises many topics like Data Mining, Data Analysis, Data Visualization, Machine Learning, Deep Learning, and most importantly it is laid on the foundation of mathematical concepts like Linear Algebra and Statistical analysis. Since there are a lot of pre-requisites for becoming a good professional Data Scientist, the perks and benefits are very big. Data Scientist has become the most sought job role these days. 

Looking for a comprehensive course on Data Science: Check out Scaler’s Data Science Course .

Useful Resources:

• Best Data Science Courses
• Python Data Science Interview Questions
• Google Data Scientist Salary
• Spotify Data Scientist Salary
• Data Scientist Salary
• Data Science Resume
• Data Analyst: Career Guide
• Tableau Interview
• Additional Technical Interview Questions

1. How do I prepare for a data science interview?

Some of the preparation tips for data science interviews are as follows:

• Resume Building: Firstly, prepare your resume well. It is preferable if the resume is only a 1-page resume, especially for a fresher. You should give great thought to the format of the resume as it matters a lot. The data science interviews can be based more on the topics like linear and logistic regression, SVM, root cause analysis, random forest, etc. So, prepare well for the data science-specific questions like those discussed in this article, make sure your resume has a mention of such important topics and you have a good knowledge of them. Also, please make sure that your resume contains some Data Science-based Projects as well. It is always better to have a group project or internship experience in the field that you are interested to go for. However, personal projects will also have a good impact on the resume. So, your resume should contain at least 2-3 data science-based projects that show your skill and knowledge level in data science. Please do not write any such skill in your resume that you do not possess. If you are just familiar with some technology and have not studied it at an advanced level, you can mention a beginner tag for those skills.
• Prepare Well: Apart from the specific questions on data science, questions on Core subjects like Database Management systems (DBMS), Operating Systems (OS), Computer Networks(CN), and Object-Oriented Programming (OOPS) can be asked from the freshers especially. So, prepare well for that as well.
• Data structures and Algorithms are the basic building blocks of programming. So, you should be well versed with that as well.
• Research the Company: This is the tip that most people miss and it is very important. If you are going for an interview with any company, read about the company before and especially in the case of data science, learn which libraries the company uses, what kind of models are they building, and so on. This gives you an edge over most other people.

2. Are data science interviews hard?

An honest reply will be “YES”. This is because of the fact that this field is newly emerging and will keep on emerging forever. In almost every interview, you have to answer many tough and challenging questions with full confidence and your concepts should be strong to satisfy the interviewer. However, with great practice, anything can be achieved. So, follow the tips discussed above and keep practising and learning. You will definitely succeed.

3. What are the top 3 technical skills of a data scientist?

The top 3 skills of a data scientist are:

• Mathematics: Data science requires a lot of mathematics and a good data scientist is strong in it. It is not possible to become a good data scientist if you are weak in mathematics.
• Machine Learning and Deep Learning : A data scientist should be very skilled in Artificial Intelligence technologies like deep learning and machine learning. Some good projects and a lot of hands-on practice will help in achieving excellence in that field.
• Programming: This is an obvious yet the most important skill. If a person is good at programming it does mean that he/she can solve complex problems as that is just a problem-solving skill. Programming is the ability to write clean and industry-understandable code. This is the skill that most freshers slack because of the lack of exposure to industry-level code. This also improves with practice and experience. 

4. Is data science a good career?

Yes, data science is one of the most futuristic and great career fields. Today and tomorrow or even years later, this field is just going to expand and never end. The reason is simple. Data can be compared to gold today as it is the key to selling everything in the world. Data scientists know how to play with this data to generate some tremendous outputs that are not even imaginable today making it a great career.

5. Are coding questions asked in data science interviews?

Yes, coding questions are asked in data science interviews. One more important thing to note here is that the data scientists are very good problem solvers as they are indulged in a lot of strict mathematics-based activities. Hence, the interviewer expects the data science interview candidates to know data structures and algorithms and at least come up with the solutions to most of the problems.

6. Is python and SQL enough for data science?

Yes. Python and SQL are sufficient for the data science roles. However, knowing the R programming Language can have also have a better impact. If you know these 3 languages, you have got the edge over most of the competitors. However, Python and SQL are enough for data science interviews.

7. What are Data Science tools?

There are various data science tools available in the market nowadays. Various tools can be of great importance. Tensorflow is one of the most famous data science tools. Some of the other famous tools are BigML, SAS (Statistical Analysis System), Knime, Scikit, Pytorch, etc.

Which among the below is NOT a necessary condition for weakly stationary time series data?

Overfitting more likely occurs when there is a huge data amount to train. True or False?

Given the information that the demand is 100 in October 2020, 150 in November 2020, 350 during December 2020 and 400 during January 2021. Calculate a 3-month simple moving average for February 2021.

Which of the below method depicts hierarchical data in nested format?

Which among the following defines the analysis of data objects not complying with general data behaviour?

What does a linear equation having 3 variables represent?

What would be the formula representation of this problem in terms of x and y variables: “The price of 2 pens and 1 pencil as 10 units”?

Which among the below is true regarding hypothesis testing?

What are the model parameters that are used to build ML models using iterative methods under model-based learning methods?

What skills are necessary for a Data Scientist?

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Statistics for Data Science: A Complete Guide

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Data science is all about finding meaning in data, and statistics is the key to unlocking those insights. Consider statistics as the vocabulary that data scientists employ to comprehend and analyze data. Without it, data is just a jumble of numbers.

A strong background in statistics is essential for anyone hoping to work as a data scientist. It’s the tool that empowers you to turn raw data into actionable intelligence, make informed decisions, and drive real-world impact. In this guide, we’ll break down the key concepts, tools, and applications of statistics in data science, providing you with the knowledge you need to succeed in this exciting field.

Fundamentals of Statistics

Statistics provides the framework for understanding and interpreting data. It enables us to calculate uncertainty, spot trends, and draw conclusions about populations from samples. In data science, a strong grasp of statistical concepts is crucial for making informed decisions, validating findings, and building robust models.

1. Descriptive Statistics

Descriptive statistics help us summarize and describe the key characteristics of a dataset. This includes measures of central tendency like mean (average), median (middle value), and mode (most frequent value), which tell us about the typical or central value of a dataset. We also use measures of variability, such as range (difference between maximum and minimum values), variance , and standard deviation , to understand how spread out the data is. Additionally, data visualization techniques like histograms, bar charts, and scatter plots provide visual representations of data distributions and relationships, making it easier to grasp complex patterns.

2. Inferential Statistics

Inferential statistics, on the other hand, allow us to make generalizations about a population based on a sample. This involves understanding how to select representative samples and how they relate to the overall population. Hypothesis testing is a key tool in inferential statistics, allowing us to evaluate whether a hypothesis about a population is likely to be true based on sample data. We also use confidence intervals to estimate the range of values within which a population parameter is likely to fall. Finally, p-values and significance levels help us determine the statistical significance of results and whether they are likely due to chance.

Why Does Statistics Matter in Data Science?

Statistics is the foundation of the entire field of data science, not just a theoretical subject found in textbooks. It’s the engine that drives data-driven decision-making, allowing you to extract meaningful insights, test hypotheses, and build reliable models.

Applications of Statistics in Data Science Projects:

Statistics is an integral part of data science projects and finds numerous applications at each stage of such projects, from data exploration to model building and validation. Here’s how:

• Data Collection: Designing surveys or experiments to gather representative samples that accurately reflect the target population.
• Data Cleaning: Identifying and handling outliers, missing values, and anomalies using statistical techniques.
• Exploratory Data Analysis (EDA): Summarizing data, visualizing distributions, and identifying relationships between variables using descriptive statistics and graphs.
• Feature engineering: Selecting and transforming variables to improve model performance, often based on statistical insights.
• Model Building: Using statistical models like linear regression, logistic regression, or decision trees to make predictions or classify data.
• Model Evaluation: Assessing the accuracy and reliability of models using statistical metrics like R-squared, precision, recall, and F1 score.
• Hypothesis Testing: Formulating and testing hypotheses about relationships between variables to draw valid conclusions.
• A/B Testing: Comparing the performance of different versions of a product or website to determine which one is more effective, using statistical significance tests.

Examples of Statistical Methods in Real-world Data Analysis:

Here are some examples of how statistical methods are applied in real-world data analysis:

• Healthcare: Statistical methods can be used for analyzing clinical trial data to determine the effectiveness of a new drug or treatment.
• Finance: Building risk models to assess the creditworthiness of borrowers.
• Marketing: Identifying customer segments and predicting their buying behaviour.
• E-commerce: Personalizing product recommendations based on customer preferences.
• Manufacturing: Optimizing production processes to reduce defects and improve efficiency.

By applying statistical methods, data scientists can uncover hidden patterns in data, make accurate predictions, and drive data-driven decision-making across various domains. Whether it’s predicting customer churn, optimizing pricing strategies, or detecting fraudulent activity, statistics play a pivotal role in transforming raw data into actionable insights.

The Fundamental Statistics Concepts for Data Science

Statistics provides the foundation for extracting meaningful insights from data. Understanding these key concepts will empower you to analyze data effectively, build robust models, and make informed decisions in the field of data science.

1. Correlation

Correlation quantifies the relationship between two variables. The correlation coefficient, a value between -1 and 1, indicates the strength and direction of this relationship. A positive correlation means that as one variable increases, so does the other, while a negative correlation means that as one variable increases, the other decreases. Pearson correlation measures linear relationships, while Spearman correlation assesses monotonic relationships.

2. Regression

Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. Linear regression models a linear relationship, while multiple regression allows for multiple independent variables. Logistic regression is used when the dependent variable is categorical, such as predicting whether a customer will churn or not.

Bias refers to systematic errors in data collection, analysis, or interpretation that can lead to inaccurate conclusions. Selection, measurement, and confirmation bias are examples of different types of bias. Mitigating bias requires careful data collection and analysis practices, such as random sampling, blinding, and robust statistical methods.

4. Probability

Probability is the study of random events and their likelihood of occurrence. Expected values, variance, and probability distributions are examples of fundamental probability concepts. Conditional probability and Bayes’ theorem allow us to update our beliefs about an event based on new information.

5. Statistical Analysis

Statistical analysis is the process of testing hypotheses and making inferences about data using statistical techniques. Analysis of variance (ANOVA) compares means between multiple groups, while chi-square tests assess the relationship between categorical variables.

6. Normal Distribution

Numerous natural phenomena can be described by the normal distribution, commonly referred to as the bell curve. It is a common probability distribution. It’s characterized by its mean and standard deviation. Z-scores standardize values relative to the mean and standard deviation, allowing us to compare values from different normal distributions.

By mastering these fundamental statistical concepts, you will be able to analyze data, identify patterns, make predictions, and draw meaningful conclusions that will aid in data science decision-making. 

Statistics in Relation To Machine Learning

While machine learning frequently takes center stage in data science, statistics is its unsung hero. Statistical concepts underpin the entire machine learning process, from model development and training to evaluation and validation. Understanding this connection is essential for aspiring data scientists and anyone seeking to harness the power of machine learning.

The Role of Statistics in Machine Learning:

Statistics and machine learning are closely intertwined disciplines. Here’s how they relate:

• Model Development: Machine learning models are created and designed using statistical methods such as regression and probability distributions. These models are essentially mathematical representations of relationships within data.
• Training and Optimization: Statistical optimization techniques, such as gradient descent, are used to fine-tune the parameters of machine learning models, enabling them to learn from data and make accurate predictions.
• Model Evaluation: Statistical metrics like accuracy, precision, recall, and F1 score are used to assess the performance of machine learning models. These metrics help data scientists select the best-performing model and identify areas for improvement.
• Hypothesis Testing: Statistical hypothesis testing determines whether the observed results of a machine learning model are statistically significant or simply random.
• Data Preprocessing: Statistical techniques like normalization and standardization are applied to prepare data for machine learning algorithms.

Examples of Statistical Techniques Used in Machine Learning:

Certainly, many statistical techniques form the backbone of machine learning algorithms. Here are a few examples:

• Linear Regression: A statistical model used for predicting a continuous outcome variable based on one or more predictor variables.
• Logistic Regression: A statistical model used for predicting a binary outcome (e.g., yes/no, true/false) based on one or more predictor variables.
• Bayesian Statistics: A probabilistic framework that combines prior knowledge with observed data to make inferences and predictions.
• Hypothesis Testing: A statistical method for evaluating whether a hypothesis about a population is likely to be true based on sample data.
• Cross-Validation: A technique for assessing how well a machine learning model will generalize to new, unseen data.

Statistical Software Used in Data Science

Data scientists have access to a vast collection of statistical software, each with its own set of strengths and capabilities. Whether you’re just starting your data science journey or you’re a seasoned professional, familiarizing yourself with these tools is essential for efficient and effective data analysis.

• Excel: While often overlooked, Excel remains a powerful tool for basic data analysis and visualization. Its user-friendly interface and built-in functions make it accessible for beginners, while its flexibility allows for custom calculations and data manipulation.
• R: It is a statistical programming language specifically designed for data analysis and visualization. It boasts a vast collection of packages and libraries for various statistical techniques, making it a favorite among statisticians and data analysts. 
• Python: Known for its versatility and ease of use, Python has become the go-to language for data science. It offers a rich ecosystem of libraries like NumPy (for numerical operations), pandas (for data manipulation and analysis), SciPy (for scientific computing), and stats models (for statistical modeling), making it a powerful tool for data scientists.
• MySQL: It is a popular open-source relational database management system (RDBMS), is widely used to store and manage structured data. Its ability to handle large datasets and perform complex queries makes it essential for data scientists working with relational data.
• SAS: It is a comprehensive statistical analysis software suite used in various industries for tasks like business intelligence, advanced analytics, and predictive modeling. It offers a wide range of statistical procedures, data management tools, and reporting capabilities.
• Jupyter Notebook: A web-based interactive computing environment that allows data scientists to create and share documents that combine code, visualizations, and narrative text. It’s a popular tool for data exploration, prototyping, and collaboration.

The software used is frequently determined by the task at hand, the type of data, and personal preferences. Many data scientists use a combination of these tools to leverage their strengths and tackle diverse challenges.

Practical Applications and Case Studies

Statistics isn’t just theoretical; it’s the engine powering many of the most impactful data science applications across industries. Here are a few examples where statistical methods play a pivotal role:

1. Customer Churn Prediction (Telecommunications):

A telecommunications company was experiencing a high rate of customer churn, losing valuable revenue. Data scientists tackled this problem by building a logistic regression model using historical customer data. This model analyzed various factors, including call patterns, data usage, customer service interactions, and billing history, to predict the likelihood of each customer churning. Armed with these predictions, the company could proactively reach out to high-risk customers with personalized retention offers and tailored services, ultimately reducing churn and improving customer loyalty.

2. Fraud Detection (Finance):

A financial institution was losing millions of dollars annually due to fraudulent transactions. To combat this, data scientists implemented anomaly detection algorithms based on statistical distributions and probability theory. These algorithms continuously monitored transaction data, flagging unusual patterns or outliers that could indicate fraudulent activity. This allowed the institution to investigate and block potentially fraudulent transactions in real time, significantly reducing financial losses.

3. Disease Prediction (Healthcare):

In the realm of healthcare, data scientists are using survival analysis and predictive modeling techniques to predict the risk of diseases like diabetes and heart disease. By analyzing patient data, including demographics, medical history, lifestyle factors, and genetic information, these models can identify high-risk individuals. Armed with this knowledge, healthcare providers can offer personalized preventive care and early interventions, potentially saving lives and improving overall health outcomes.

4. Recommender Systems (e-commerce):

E-commerce giants like Amazon and Netflix rely heavily on recommender systems to drive customer engagement and sales. These systems use collaborative filtering and matrix factorization, statistical techniques that analyze vast amounts of user behavior and product/content data. By understanding user preferences and item characteristics, recommender systems can suggest products or movies that are most likely to resonate with each individual, resulting in personalized shopping experiences and increased revenue.

These case studies demonstrate how statistics enables data scientists to tackle complex problems, uncover hidden patterns, and provide actionable insights that drive business value across industries. By leveraging statistical methods, you can create innovative solutions that have a real-world impact, from improving customer satisfaction to saving lives.

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Statistics is the foundation on which data science is built. It provides the essential tools for understanding, analyzing, and interpreting data, allowing us to uncover hidden patterns, make informed decisions, and drive innovation.

From the fundamental concepts of descriptive and inferential statistics to the advanced techniques used in machine learning, statistics empowers data scientists to transform raw data into actionable insights. By mastering the concepts discussed in this guide, you’ll be well-equipped to tackle the challenges of data analysis, build robust models, and make data-driven decisions that have a real-world impact. Remember, statistics is not just a subject to be studied; it’s a powerful tool that can unlock the full potential of data and propel your career in data science to new heights.

If you’re ready to dive deeper into the world of data science, consider exploring Scaler’s comprehensive Data Science Course . They offer a well-structured curriculum, expert instruction, and career support to help you launch your career in this exciting field.

What statistics are needed for data science?

Data science requires a solid foundation in descriptive and inferential statistics, including measures of central tendency and variability, probability distributions, hypothesis testing, regression analysis, and sampling techniques.

What are the branches of statistics?

The two primary branches of statistics are descriptive statistics, which summarize and describe data, and inferential statistics, which draw conclusions about populations from samples. Other branches include Bayesian statistics, non-parametric statistics, and robust statistics.

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Statistics is important in data science because it provides tools for analyzing and interpreting data, developing reliable models, making informed decisions, and effectively communicating findings. It’s the backbone of the entire data science process, from data collection to model evaluation.

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• Open access
• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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• Human behaviour
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An Author Correction to this article was published on 07 May 2024

This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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Contributions

All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Correspondence to Fabian Braesemann .

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The original online version of this Article was revised: The Data Availability section in the original version of this Article was incomplete, the link to the GitHub repository was omitted. Full information regarding the corrections made can be found in the correction for this Article.

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McCarthy, P.X., Gong, X., Braesemann, F. et al. The impact of founder personalities on startup success. Sci Rep 13 , 17200 (2023). https://doi.org/10.1038/s41598-023-41980-y

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DOI : https://doi.org/10.1038/s41598-023-41980-y

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