data science take home assignment examples

Six Steps to Pass the Data Science Take Home Challenge

How many times have you seen these exact words? Hopefully not much in your data science interview process. But if you’re one of many data scientists looking for a job, you might find yourself working on a data science take-home assignment in a zipped file with a requirements pdf that’s ten pages long.

The recruiter promises that there’s an intricate grading process on your assignment that shouldn’t take more than a couple of hours. But suddenly it’s 2:30 AM, three days later, 15 hours of coding exhaustion put in, and you haven’t even thought about trying that GAM model to see it improve your model’s F1 score by three percent. Why is this sort of thing continuing to happen? Why are companies wasting the candidate’s time still without any sort of feedback on the take-home?

The truth is that the process works as a filter for many companies that don’t have a standardized interview process. Development of technical interview questions requires a history of knowledge on the part of data science teams that just does not exist yet compared to software engineers.

So if you must, have to, and there’s no other choice, to do a take-home assignment. Here are a few steps to take to ensure a smoother process.

• Understand expectations
• State assumptions everywhere
• Do the modeling basics
• Make the take-home challenge readable
• Write Tests and Comments
• Summarize your thought process

1. Understand Expectations

It’s difficult to push against a company who is interviewing you or going to be interviewing you. But understanding the full expectations of the data science take-home challenge will be the key to passing it successfully.

Here’s an email template to use with the recruiter.

Hi Recruiter’s Name,

Thanks for sending over the take-home assignment. I’m excited to start it and will be sure to send it back in X days with my completed solution.

Additionally, I was wondering if I could be provided with a set of general guidelines on how the assignment will be graded. I definitely want to be sure I’m focusing and demonstrating the correct skillset for the take-home and not accidentally going down a rabbit hole.

Lastly, I would really appreciate it if after I send in my take-home assignment that I could get some feedback on it, regardless of whether or not I move on in the interview process. It would really mean a lot to understand what I did wrong or where I excelled for my own technical growth.

2. State Assumptions Everywhere

Try to immediately tally up a list of questions that you can send to the recruiter/hiring manager after receiving the take-home challenge. Even after getting answers to your questions or receiving no answer, make sure to then state your assumptions in your data science take-home challenge. What do I mean by that?

What if you decide to only use a naive imputation model to fill in missing values instead of an advanced technique? State it. Write it in a comment. Do something where they understand your limitations to the amount of time you’re spending on the assignment.

Write up everything that you think needs to be known to your grader. Hiring managers forgot how long it took to write code and build models. They’re managers. They don’t write code.

3. Do the Modeling Basics

Here’s a general checklist that will probably take you at least a minimum of three hours.

• Data cleaning
• Minimal feature selection
• Impute missing values
• Create a classification pipeline
• Try training with a couple of sci-kit learn classifiers
• Tune hyperparameters with grid-search

Boom. Now your implementation will reach the general minimal baseline of what they’re expecting. Dependent on how long you work on feature selection, it could go plus or minus an extra two to three hours.

4. Make the Take-Home Challenge Readable

Here’s a great guide toward code organization and readability for data scientists. It’s about structuring your project in an easy-to-digestible manner. I stumbled upon this randomly, but it completely makes sense. The Cookiecutter data science framework allows for a standardized process for data science projects. Taken directly from their website:

• Collaborate more easily with you on this analysis
• Learn from your analysis about the process and the domain
• Feel confident in the conclusions at which the analysis arrives

I will note that it will definitely take you more than a few hours to organize your project with the complete format. But then again you already understood the cost when you decided to do a data science take-home assignment.

5. Write Tests and Comments

Did I mention documenting everything in your head onto paper? That includes writing comments and testing your code if it’s applicable. Readability is as important as the efficiency of your code and if you write nice comment blocks on each function, it will help communicate how your code should function and why you re-factored it the way you did. Follow the general Python conventions to make sure you’re solid.

6. Summarize Your Thought Process

Remember in high school English when all papers consisted of an introduction, content, and then conclusion, which repeated the introduction? Do that but in under 500 words. At the end of the day, the most likely scenario is that the person looking at your take-home assignment will spend a grand total of five minutes of their time understanding it before moving on back to browsing Reddit. You want to make it as easy as possible for them to understand your data science take-home challenge as being the best possible take-home challenge ever.

From the creator of "A Collection of Data Science Take-home challenges"

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Data Science Take Home Assignments used in Sandvik CODE's recruitment process.

sandvikcode/data-science-take-home

Folders and files, repository files navigation, data science take home assignment, introduction.

Welcome to Sandvik CODE's take-home assignment repository, part of our data science recruitment process. The following assignment will let you extract, explore and analyze audio data from English speaking male and females, and build learning models aimed to predict a given person's gender using vocal features, such as mean frequency, spectral entropy or mode frequency.

Contrary to most online communities that share datasets for data science, machine learning and artificial intelligence applications, readymade datasets rarely exist out in the wild, and you will have to explore one or more ways of downloading and extracting meaningful features from a raw dataset containing thousands of individual audio files. The assignment is designed to test your ability to tackle the full range of responsibilities you will have as a Data Scientist at Sandvik CODE.

The raw data consists of, as of 30th August 2018, 95,481 audio samples of male and female speakers speaking in short English sentences. The raw data is compressed using .tgz files. Each .tgz compressed file contains the following directory structure and files:

• GPL_license.txt
• prompts-original
• 10 unique .wav audio files

The total size of the raw dataset is approximately 12.5 GB once it has been uncompressed. The file format is .wav with a sampling rate of 16kHz and a bit depth of 16-bit. The raw dataset can be found here .

We recommend considering the following for your data pre-processing:

• Automate the raw data download using web scraping techniques
• Pre-process data using audio signal processing packages such as WarbleR , TuneR , seewave for R, or similar packages for other programming languages
• Consider, in particular, the human vocal range , which typically resides within the range of 0Hz-280Hz
• To help you on your way to identify potentially interesting features, consider the following (non-exhaustive) list:
• Mean frequency (in kHz)
• Standard deviation of frequency
• Median frequency (in kHz)
• First quantile (in kHz)
• Third quantile (in kHz)
• Inter-quantile range (in kHz)
• Mode frequency
• Peak frequency
• Make sure to check out all of the files in the raw data, you might find valuable data in files beyond the audio ones

Question Set

The following are reference points that should be taken into account in the submission. Please use them to guide the reasoning behind the feature extraction, exploration, analysis and model building, rather than answer them point blank.

• How did you go about extracting features from the raw data?
• Which features do you believe contain relevant information?
• How did you decide which features matter most?
• Do any features contain similar information content?
• Are there any insights about the features that you didn't expect? If so, what are they?
• Are there any other (potential) issues with the features you've chosen? If so, what are they?
• Which goodness of fit metrics have you chosen, and what do they tell you about the model(s) performance?
• Which model performs best?
• How would you decide between using a more sophisticated model versus a less complicated one?
• What kind of benefits do you think your model(s) could have as part of an enterprise application or service?

You have 10 days to complete the assignment from the time that you have received the email containing the link to this GitHub repository.

Note: Your submission will be judged with this timeframe in mind, and we do not expect the equivalent of a month's worth of work.

Requirements

A written presentation, in HTML or PDF format , that clearly and succinctly walks us through your approach to extracting features, exploring them, uncovering any potential constraints or issues with the data in its provided form, your choice of predictive models and your analysis of the models' performance. Try to keep it concise.

A good presentation presents potential caveats, findings and insights about the dataset and an analysis of the goodness of fit metrics, including benchmarking on the performance of different learning models.

A great presentation tells a visual, potentially even interactive, story about the data and how specific insights can be used to guide our product development so that non-technical colleagues can understand and act upon them.

Please send your presentation (see Requirements below)to digitaljobs [at] sandvik.com , with it as an attachment, or provide us with a link to where you've uploaded your work (in case of a private git repository, server or similar).

Happy coding!

Code of Conduct

At Sandvik CODE, we give the people we hire a high level of autonomy in how they want to work, and value personal integrity highly. This autonomy only works if we can trust our colleagues to be honest about the work they are responsible for and the results they achieve. We trust that you do not collaborate with other people as part of this assignment, nor that you make use of readymade machine learning models without being able to properly argue for why they are applicable. The same goes for your choice of features and understanding of what type of information they provide.

If we realize, at any point during the recruitment process or following a successful hiring, that you have broken the trust we now place in you, we will terminate your application or employment immediately.

By accepting this take-home assignment, you are actively choosing to abide to this code of conduct.

Frequently Asked Questions

• Can I use <Insert SDK or Framework here> for the take home assignment?

Answer: Yes, you are free to make use of the tools that you are most comfortable working with. We work with a mix of frameworks, and try to use the one best fit for the task at hand.

• Where do I send my assignment upon completion?

Answer: You should have received an email with instructions about this take home assignment that led you to this repo. If you have been invited to the take home assignment, but haven't received an email, please email us at digitaljobs[at]sandvik.com .

• The raw data is too large to fit in memory, what do I do?

Answer: This is part of the challenge, and the dataset is by design larger than can fit in memory for a normal computer. You will have to come up with a solution that enables processing of the data in a batch-like, or streaming, fashion, to extract meaningful features.

• Where do I send my presentation of my results?

Answer: Please send it to digitaljobs [at] sandvik.com . In case you've uploaded your work to a git repository, whether on GitHub or elsewhere, please invite us to it for evaluation.

Contributors 3

A Collection of Take-Home Data Science Challenges for 2024

A collection of take-home data science challenges that both beginners and professionals can refer to.

Challenges make us all uncomfortable but none of us can deny that difficult challenges only help us bring out the stronger and better version of ourselves. So, if you are a professional data scientist or an enthusiast, read this article for a collection of take-home Data Science Challenges and develop better skills by attempting them. data science team  

Working on take-home data science challenges is equally important for professionals and beginners alike. Beginners can use such challenges to realise how industries implement data science to solve practical problems while professionals can work on such challenges to upskill. Additionally, solving a collection of take-home data science challenges is a good way of learning data science as it is relatively more engaging than other learning methods. Furthermore, solving difficult problems in data science not only prepares you for the future but also teaches you the latest tools, techniques, algorithms and packages that have been introduced in the industry.

End-to-End Speech Emotion Recognition Project using ANN

Downloadable solution code | Explanatory videos | Tech Support

So, continue reading this blog as we have prepared an exciting list of data science take-home challenges for you.

Table of Contents

Latest data science take-home challenges that you must try, solved data science take home challenges for beginners, data science take-home challenges for interview preparation, how to do well on take-home data science challenges.

The challenges have been divided into three categories for simplicity. 

The first one contains challenges that have been recently launched on Kaggle.

The second category has challenges with solution code. You may refer to these solutions to solve the problems introduced in the first section.

The third section contains sample data science take-home challenges for interview preparation to help you prepare for your dream job.

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

Here are five of the most recent data science take-home challenges on Kaggle that you must try in 2022.

Crypto Forecasting

Cryptocurrencies and blockchains are expected to be the future of the global economy. With Bitcoin witnessing initial success, many investors consider cryptocurrency as an asset for their portfolio. However, on the flip side, many investors believe it is a bad idea considering how volatile the exchange prices of cryptocurrencies are. 

One way to help the investors is to give them a fair idea of the risks involved by predicting the returns using machine learning. And that’s exactly what this Kaggle challenge is all about. You must try to use the advanced technology of machine learning and estimate the short-term returns for 14 popular cryptocurrencies. So, if this sounds exciting to you, we recommend you check out the official link to know more about this data science challenge. Final Submission Deadline: February 1, 2022 Prize Money for the first rank: $50,000

Kaggle Challenge Link: G-Research Crypto Forecasting

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Scheduling Christmas-Movie Marathon

For many people, Christmas is the best time of the year. So, why not have a television channel with a Christmas vibe 24/7, and one can simply tune in to the channel whenever they want to surround themselves with those Christmassy feelings? If you like the idea, get ready with your machine learning tools because this challenge involves helping the Christmas elves launch a channel by the name, SantaTV+. There are about seven movies that the elves at SantaTV+ want to air, and your task is to decide the order in which the film will give the perfect Christmas vibes. There are a few constraints on the order of the movies, and we suggest you visit the link to know more.

Final Submission Deadline: January 11, 2022 Prize Money for the first rank: $5,000

Kaggle Challenge Link: Santa 2021 - The Merry Movie Montage

Time Series Forecasting

You may run miles and pause your location coordinates in the 3-dimensional space, but that may not be the case with time. Time is eternal, and most businesses are interested in chasing it. Corporación Favorita is a large grocery retailer in Ecuador. The challenge is to create a model that can best estimate the unit sales for thousands of items sold at their different stores. By implementing various machine learning algorithms over a dataset of dates, store, item information, promotions, and unit sales, you will be using time forecasting methods to predict the sales.

Final Submission Deadline: None Prize Money for the first rank: None

Kaggle Challenge Link: Store Sales - Time Series Forecasting | Kaggle

Medical Image Segmentation

Over the past few years, neurodegenerative diseases like Parkinson’s and Alzheimer’s have proven to be fatal and become a cause of disability worldwide. And, scientists working at various pharmaceutical companies are thus trying to utilize artificial intelligence technology for improving its diagnosis so that medical practitioners can save many more human lives.

The challenge is to detect different neuronal cells in the given set of microscopic images using computer vision. Because these cells have quite irregular, unique, and concave morphology linked with them, it is pretty challenging to identify them. So, the goal is to use phase-contrast microscopy images and detect the neuronal cells with a high level of accuracy through deep learning algorithms. A robust model will assist in the discovery of new drugs to treat various neurological disorders.

Final Submission Deadline: December 30, 2021 Prize Money for the first rank: $25,000

Kaggle Challenge Link: Sartorius - Cell Instance Segmentation | Kaggle

Starfish Identification

The Great Barrier Reed foundation needs your help in protecting the world’s largest coral reef ecosystem. One of the reef fishes, coral-eating crown-of-thorns starfish (COTS), has outnumbered itself in a way that disrupts the reef’s healthy ecosystem.  Their population thus has to be controlled so that the reef continues to be home to various other species.

This challenge is about implementing deep learning object detection models over the thousands of images collected by the underwater camera. The model should be able to alienate COTS from the other 2000+ different species that are housed by the reefs. The TensorFlow team at Google is sponsoring the competition, so keep an eye out for the exciting prizes.

Final Submission Deadline: February 14, 2022 Prize Money for the first rank: $30,000

Kaggle Challenge Link: TensorFlow - Help Protect the Great Barrier Reef | Kaggle

New Projects

If you are new to data science take-home challenges, we suggest you work on the following solved data science challenges to get a sense of how such problems are solved. Going through these solutions will help you understand how to approach complex problems in data science rightly.

1. Two Sigma Financial Modeling Challenge

If you are passionate about exploring the applications of data science techniques in economics and finance , you will especially enjoy working on this challenge. Two Sigma Investments is a firm implementing data science tools over datasets for predicting financial trade since 2001. They tied up with Kaggle to launch a competition in 2017 and asked the data science community to help them improve the estimates of financial movements. The exciting part about this challenge is that it was the first time a code competition was launched on Kaggle.

Complete Solution: Two Sigma Financial Modeling Challenge Solution

2. Give Me Some Credit Challenge

Banks are one the key institutions that play a significant role in the overall economic growth of any country. Banks allow many businesses to evolve by providing them with the necessary funds at a fair interest rate. And to decide which individual or firm should be allowed to lend money or not, banks use credit scoring algorithms. These algorithms estimate the probability of a lender defaulting on the loan repayments and help them make better decisions. This challenge aims to build a model that can best predict the future of a money-lending individual/firm.

Complete Solution: Credit Score Prediction Challenge Solution

3, Zillow’s Home Value Prediction Challenge

‘Home is where the heart is.’ This proverb basically highlights that we all feel at home near our loved ones. That is why when it comes to buying a house where they will spend most of the time with their loved ones, many buyers take a lot of time to collect information about prospective homes. Additionally, as a house is one of the most expensive things a person purchases in their lifetime, it becomes essential to analyze this asset properly. To help such buyers, Zillow created Zestimate that evaluates the right selling price by processing all information it has about a house. In 2017, Zillow launched the Zillow Price challenge, asking the Kaggle users to improve the accuracy of the Zestimates. The task is to build a model that results in a reduction in the residual error which is defined by

Complete Solution: House Price Prediction Project using Machine Learning

4. Mercari Price Suggestion Challenge

Like consumers want to make sure that they buy a product at its best price, sellers also try hard to ensure that they are offering the best price. Mercari is a community-driven shopping app in the United States that wanted data science enthusiasts like you to help its users decide the right price. The users will submit the necessary details about the products they want to sell, and the model should then be able to suggest prices automatically.

Complete Solution: Retail Price Recommendation with R

5. Estimating Avocado Prices Challenge

Do you know about 45 per cent of the avocados available in the international market are grown in Mexico? In fact, the label “Avocados from Mexico” is often used as a click-bait on social media. Avocado is gradually becoming a popular fruit in the United States and one organization working for it is the Hass Avocado Board (HAB). They have data about avocados that anyone can use for research and analysis. Using their data, one can analyze various trends prevailing in the avocado market and explore the gravity of #2017Avocadolypse. 

While you can not find it as a challenge on Kaggle, it is reasonably popular among beginners to use this data and predict the prices of avocados in the future. Thus, we recommend you take on the challenge of using a machine learning model for estimating future avocado prices and market trends.

Complete Solution: Avocado Machine Learning Project python for Price Prediction

Get FREE Access to Machine Learning Example Codes for Data Cleaning , Data Munging, and Data Visualization

6. Titanic - Machine Learning from Disaster

The titanic movie beautifully depicted the romance between the two lovebirds, Jack and Rose. Along with that, the movie also highlighted the socioeconomic differences that are usually present in most societies. Keeping them in mind, one can quickly feel intrigued to ask whether those differences have anything to do with their chances of surviving the ship. If you are interested in knowing more about it, check out this challenge that wants you to predict the surviving probabilities of different passengers on the ship.

Complete Solution: Kaggle Data Science Challenge -Predicting survival on the Titanic (projectpro.io)

7. Predict Red Wine Quality

Most people believe that having red wine in moderation can be good for their health. But, to have your doctors support this thought, you must be sure of its quality. By working on this data science challenge of wine quality prediction, you can understand what factors affect the quality of red wine. The dataset for this challenge is available on the UCI Machine Learning Repository . Work on this challenge for understanding regression and classification machine learning algorithms .

Complete Solution: Wine Quality Prediction using Machine Learning in Python

8. Two Sigma Connect: Rental Listing Inquiries

We already have introduced the company, Two Sigma, in the first challenge of this section. Here is another challenge hosted by the same firm in collaboration with RentHop. On RentHop, one can find rent houses of their dreams. But, a few listings on the website might seem too good to be true, and your task is to detect those fraud listings. It will assist owners and agents in understanding the perspective of different renters.

Complete Solution: Predict the interest level of a given rental listing on RentHop (projectpro.io)

9. Walmart Recruiting- Store Sales Forecasting

Data Science methods are rapidly becoming popular in the retail industry because of the transformation taking place because of those methods. While it is easy to use machine learning algorithms to predict store sales on typical days, it becomes quite challenging to estimate the sales for the holiday season. One reason for this could be the various discounts retailers provide their customers on such special occasions. But, what are the possible reasons behind it? Work on this data science challenge to find out. The goal is to use the given data of 45 Walmart stores and predict their sales for each category of products in each store. A fun fact about this data science challenge on Kaggle is that Walmart used this competition to recruit members for its data science team.

Complete Solution: Walmart Store Sales Forecasting Challenge

10. CVC-ClinicDB

CVC-ClinicDB is a collection of images captured from colonoscopy videos. In case you don’t know, colonoscopy refers to a medical test that aims to track down any unusual changes in the large intestine (colon). Medical practitioners use a fiber optic camera attached to a flexible wire to perform this test, and these videos are then analyzed to identify the abnormalities. For this challenge, you have a database of two types of images, one is a set of unedited clicks, and the other has a region of unusual tissue growth, polyp, masked. Your task is to train a deep learning model to identify the polyp regions from the test images.

Complete Solution: Medical Image Segmentation Deep Learning Project

Explore Categories

While preparing for a data science interview , you must explore more practical data science challenges. In other words, the data should be raw and less organized as in the real world, a data scientist hardly gets a clean dataset. Instead, they perform Exploratory Data Analysis (EDA) on the given data and then filter the attributes with greater significance to the objective. Thus, to help you get more interview-ready, we have prepared a list of Data Science case studies that you can try before applying for jobs.

Fault Detection Data Science Take-Home Challenge

Because of the Internet of Things (IoT) technology, many vehicles are now embedded with different sensors that send data to the manufacturing company. The company’s data science team then analyses it to predict which parts of the vehicle are likely to wear and tear soon. Thus, you are likely to encounter such challenges when appearing for an interview at companies like Tesla, Mercedes, etc.

Problem: Consider a dataset that contains the following attributes of the vehicle:

Vehicle Model: The model id of the vehicle to help identify the vehicle type

Battery Voltage: Float value for voltage of the vehicle battery;

Longitude: Float value for the vehicle longitude;

Latitude: Float value for the vehicle latitude;

Ignition: Boolean value indicating whether the ignition is

switched on;

Odometer: Float value for the vehicle odometer;

GPS Signal: Boolean value indicating whether the GPS signal is valid;

Velocity: Float value for the vehicle speed;

Memory index: The integer value for the point’s memory position is saved in the module.

These features are collected from thousands of sensors connected to the vehicle. Your task is to predict the state of each sensor. Here are the possible outcomes: 

A: Accelerometer defective;

B: GPS with jumps in location;  

C: Wrong pulse set up;

D: Ignition wire released;

E: GPS locked;

F: Module buffer with a problem;

G: Odometer locked;

H: No issues.

Answer the following questions: 

Q1. Do you plan to use both latitude and longitude along with the boolean variable for GPS to check if it’s working correctly or not?

Q2. What new attributes can you create from the already existing ones?

Q3. Considering this is a multi-class classification problem, which algorithms are you likely to use?

Sentiment Analysis Data Science Take-Home Challenge

Any company that wants to stay in business for long highly values their customers’ feedback. Thus, this challenge is a must for any company you are targeting. 

Problem: Consider the Amazon Customer Reviews Dataset and answer the following questions: 

Q1. Which are the top 10 most liked and disliked products in the dataset?

Q2. For which products the customer reviews are likely to increase in the next 30 days?

Q3. The customer reviews are likely to decrease in the next 30 days for which products?

Q4. Which products do not seem to have genuine customer reviews?

In addition, you may also implement a web scraping project idea and extract review data of your target company from Twitter to build your own dataset. 

Hint: Use NLP methods like lemmatization, stemming, etc., to perform sentiment analysis over the product reviews and then use machine learning algorithms to make the required predictions. 

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Recommendation System Data Science Take-Home Challenge

Using customer data to understand their preferences for improving their experience is a norm these days. Many websites achieve it by building recommendation systems that analyze customer behavior and provide them with a plausible list of products that they are likely to fall for. Companies like Netflix and Spotify use such systems to increase engagement, while Amazon and Airbnb use them to help their users make a more conscious choice.

Problem: Use the UCI Machine Learning Repository: Online Retail Data Set and build a recommendation system for a typical user.

Hint: Explore the applications of APRIORI and collaborative filtering algorithms in creating a recommendation system.

Marketing Strategy Data Science Take-Home Challenge

Often companies look for a data scientist specifically to help them enhance their marketing policy. They expect a data scientist to help them improve their sales by suggesting innovative changes in their current strategy.

Problem: Consider the dataset of a firm that contains the following basic information about their customers:

Name, Age, Location, Gender, Products Purchased, Order date, Order Time, Order Day.

Use the dataset to answer the following questions for the company’s marketing team:

Which cities should be targeted more for advertisements?

Which users should be receiving more frequent SMS text messages about recently launched products?

At what time and day of the week should promotions be promoted more than the other days?

Hint: Use various supervised and unsupervised machine learning methods to answer the questions correctly.

Explore More  Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

Bot Detection Data Science Take-Home Challenge

With artificial intelligence technology, as more and more tasks become automated, it has become difficult to identify whether a task performer is a machine or a human. And, the reason why bot identification is of interest to businesses is their ability to degrade the company’s reputation by sending spammy emails with malicious intent, stealing customers’ data, etc. So, work on the challenge below to find out how to use data science for bot identification.

Problem: Suppose you are given a dataset of 5K tweets with the following attributes:

Twitter Username, Date of the Tweet, Time of the tweet, Tweet Content, Location of the Twitter User, Number of Log-In(s) in a day.

Use data preprocessing methods and train a machine learning model to differentiate between a bot and a human user. 

Hint: Bot users usually have patterns in their time of uploading a tweet. Additionally, use NLP methods to draw insights from the content of the information.

ChatBot Data Science Take-Home Challenge 

Chatting Robots or Chatbots seem to have revolutionized customer experience for most businesses. They are quick in resolving simple customer queries and ping the customer support team of a company only when human intervention is necessary. As more and more companies are opting for one, you are also likely to be asked to build a chatbot as part of a data science interview.

Problem: Consider a food chain store that runs about ten stores across Delhi, India. They want you to create a chatbot that will allow their customers to order directly using the bot application. The bot should try to understand the customers’ preferences and assist them in placing orders online.

Hint: List various categories of the food menu to the customer instead of presenting the full menu at once. Additionally, in the end, the app should be able to list and identify product bundles to help the customers make better decisions.

Logs Analysis Data Science Take-Home Challenge

Whenever an individual visits a website, machines in the backend store log files containing information about events within an operation system, usage patterns, etc. These files help businesses understand user behavior and preferences. Try out the problem below to know how one can use machine learning for log analytics .

Problem: Consider the UCI Machine Learning Repository: Anonymous Microsoft Web Data Data Set and use machine learning algorithms to predict which regions of the Microsoft’s website the user visited. 

Hint: Use data preprocessing techniques before you move on to the application of ML algorithms.

Computer Vision Data Science Take-Home Challenge

A data scientist is not usually expected to be an expert in computer vision projects . However, you might occasionally find a few data scientists job advertisements requiring computer vision expertise. So, go through the problem below if that is the case with the job description of your target job.

Problem: Use the MNIST dataset to build a system that recognizes handwritten digits . The application should correctly identify an image that it has not been already trained for.

Hint: Look for various predefined convolutional neural network algorithms that quickly solve this problem.

Recommended Reading: 15 OpenCV Projects Ideas for Beginners to Practice

Revenue Management Data Science Take-Home Challenge

Data Science is mainly used to reveal unique patterns and trends that are hidden in a company’s data. The company leaders often utilize such trends to modify their strategies and make informed decisions. Here is a sample problem where you will learn how data science proves helpful in managing a firm’s revenue.

Problem: Consider the Grupo Bimbo Dataset on Kaggle. Use the dataset to predict the inventory demand for their 100+ bakery products. Additionally, analyze the dataset to reveal which resources are likely to be consumed the least and the most. 

Access Job Recommendation System Project with Source Code

Resume Parsing Data Science Take-Home Challenge

Here is an exciting application to test your NLP skills that will likely smoothen the hiring process of a company’s HR team.

Problem: Suppose you have ten random resumes of job applicants. Your task is to apply data mining techniques and NLP methodologies and build a resume parsing system that can point out which applicant is best suited for the role of a business analyst .

Recommended Reading: 50 Business Analyst Interview Questions and Answers

This is a ubiquitous question that data science enthusiasts ask worldwide. Follow the steps below to know how you can better solve take-home data challenges.

Start Slow: Pick up a challenge that sounds simple and interesting to you. 

Take small steps: Do not try to jump straight to difficult problems. Gradually increase the level of difficulty.

Research: Look for good resources online to help you learn new skills. Many data scientists support the beginners’ community and provide insightful details to help them.

Practice: Once you have learned about popular machine learning and deep learning algorithms , make sure to practice projects around them so that you remember everything.

Network: Connect with data science professionals on LinkedIn to seek any insights on a challenging topic that you cannot solve yourself.

If you are looking for a platform that can automatically take care of all the above pointers, we recommend you check out ProjectPro. The platform offers a customized learning path, 100+ projects on data science and big data , and allows you to connect with industry experts if you need assistance. Check out ProjectPro projects’ repository to learn more.

About the Author

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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How to Apply for a Data Science Job and How to Prepare for Interviews

Peter Scobas

• April 12, 2021

You have an awesome resume, a jaw-dropping portfolio, and a Pulitzer Prize winning cover letter. What’s next? Now we’re getting to the good stuff: applying for a data science position and preparing for the job interview(s).

For most junior data positions, the application process is relatively straightforward: after submitting your application, the recruiter will reach out and you’ll generally have a phone screen with the recruiter. Then, you’ll often be asked to complete a data science task as part of the technical screen. If you pass that, you’ll usually have a more technical conversation with the hiring manager, followed by the final stage: the onsite interview.

But let me show you the details.

In this article, I’ll discuss the following:

Where to look for job postings

How to follow up after submitting your application.

• How to prepare for the phone screen with the recruiter
• How to impress the hiring team with the technical data assignment

Before we get started…

This article is part of a five article series called: How to get a job in data science and analytics .

Here are all the articles:

• epsiode #1 — Intro: What is a data analyst/scientist and what skills do you need?
• episode #2 — What do you need to do before you apply? (resume/cover letter/website/GitHub help)
• episode #3 — How to apply and how to prepare for data science job interviews and how to ace the take-home assignment — this article
• episode #4 — Common junior data science job interview questions and how to answer them
• episode #5 — How do you negotiate? Should you negotiate? What is the career trajectory for someone in data science and analytics?

Now, it’s time to get into it:

This will be a pretty short section.

The best places to look for job postings are:

• Glassdoor ,
• AngelList , and

… generally in that order.

Note 1: if you end up using AngelList, you will need to put together a profile similar to LinkedIn. There isn’t a magical one-stop shop for all listings, but if you scour these sites you will definitely see the bulk of the job postings in data science and analytics.

Note by Tomi Mester: The above list applies to the US job market. When we are talking about Europe – in my experience – LinkedIn is pretty much the only go-to place when looking for data science positions. Of course, you can always extend your opportunities relying on your personal network. And direct applications through the website of a favorite company of yours is also possible (although it’s not common).

I consider the advice in this section somewhat optional. I would not do this for all the jobs you apply to, but following up for the select few jobs you are really interested in can make a difference.

Okay. So you submitted your application but you haven’t heard anything back. For job openings you are particularly interested in, I would do the following:

• Find the hiring manager’s email. This is generally pretty easy—you’ll just have to do some digging. You can find emails on LinkedIn, or use something like hunter . If you can’t find the hiring manager’s email, try to find someone on the data team.
• Once you have the email address, write a thoughtful email and attach your resume. Keep the email short, but interesting. Say what job you recently applied for, what you like about the company, how you believe you can make an impact, and end with a soft ask. Something like:
• Continue to follow up until you hear a response.

Alternative follow-up strategy

Another strategy that works is to message another data analyst/scientist on the team (either email or on LinkedIn), and say something like:

I would recommend you send this email out before you apply—if they never respond, apply to the job anyway. However, if they do respond, there’s a good chance you can give your resume to them and they can forward your resume to the recruiter.

I particularly like this strategy—for a few reasons. One, most people are nice—and it’s hard to turn down free coffee (quick note: buy their coffee for them). If/when you meet, keep the conversation light; ask them about their experience, what advice they have for you, and what they like about their company.

How to prepare for a phone screen interview with the recruiter

This stage of the application process — the phone screen — is also always pretty straightforward. The recruiter at this point is trying to check a few things:

• You can clearly speak about the stuff on your resume —the recruiter might ask you to explain a project you worked on that used data.
• The recruiter will most likely ask you what your programming background is—generally, they are looking for keywords like SQL , Python packages like maplotlib , Pandas , and NumPy, etc.
• Always have a few reasons for why you are leaving your current job and why you’re interested in this position/company (in a later section, I’ll go into more detail about how to answer some standard interview questions).
• Also, always have a few questions to ask the recruiter as well! Should be common sense, but definitely something that is overlooked when preparing for recruiter phone screens.
• The recruiter might ask about what salary range you’re interested in—see a later section on how to answer the salary range question.
• Be friendly and sound interested and excited. Recruiters are also trying to gauge if you’d be a “good fit.” It’s easier to check off this “good fit” box when you’re friendly and having an engaging conversation with the recruiter.

Remember: the recruiter probably does not know much about the position you’re applying for—meaning, they’re not going to ask you technical statistics questions, or questions about SQL/Python/R. They might have a list of questions they’re given to ask, like “have you worked with window functions?” or “what type of statistical modeling techniques have you used?” but the point of these is to make sure you have the skills the hiring manager is looking for.

Lastly, have an open (email) dialogue with the recruiter. If they set up a time to have you talk to the hiring manager, or come in for an onsite interview, ask them if they have any advice or anything that might be helpful to prepare for. As the application process goes into the later and later stages, recruiters want to get positions filled, and there is a good chance they will offer at least some advice. The more information you can have at each step of the process, the better.

How to impress the hiring team with the take-home data assignment

Oh, the famed take-home data task. In this section, I have a list of DOs and DON’Ts for you, as well as a conceptual discussion on how to ace the take-home assignments. (These are based on real assignments from some of the biggest tech companies.) I’ll also show you how I would approach the questions, how I would approach writing the code, and how I would present my results.

In this technical screening step, there are usually two types of tasks:

• Timed programming challenge . I do not have much advice on how to prepare for these. Most companies (that I know of) do not use this as a step in the application process, but if you find yourself having to complete one of these, just do your best and don’t stress about finishing in the allotted time—just make sure what you can finish is correct and a good representation of your programming skills.
• Data analysis challenge . This is the most frequently used technical screen. It usually involves a dataset and some questions to show off your programming skills as well as your ability to analyze and synthesize results. This section is focused on this type of technical challenge.

The data analysis challenge is used to evaluate the following:

• Can you demonstrate the technical skills you discussed in your resume?
• Are you able to handle and clean messy data?
• Is your code clean, well-written, and well-documented?
• Are you able to clearly communicate and present your results?

This data assignment phase is where you can initially stand out in the interview process.

If you follow my advice, you can set yourself apart from the other applicants with a strong data assignment. Other than the resume/cover letter step, this is the one phase of the application process you have complete control over. You can generally spend as much time on it as you want, troubleshoot your code, look at resources online if you need to, and think through how you want to approach the task and how you want to present your work.

DOs and DON’Ts for the data science take-home assignment

So with that said, here are some DOs and DON’Ts for how to be successful in the take-home assignment stage:

• DO : if you’re really interested in the job, finish the assignment. Not sure why this is the case—but a fair number of the assignments I review are incomplete. If you really want to make a strong impression, finish the assignment.
• DO : make it extremely easy for the team to review your assignment. In 99.99% of cases, all you need to send back are two files: one text-file of your code, and one PDF with the questions you had to answer in bold, and your answers/visualizations/results below.
• DON’T : send a Jupyter notebook, html file, etc. You run the risk of the formatting being off and it will look disjointed and clunky. Just think about this: if you get hired, your job would be to take a question, use data to answer that question, and present and/or communicate your findings to non-technical people. You’re not going to send someone on the marketing team a Jupyter notebook where they have to sift through the code.
• DON’T : send your code without a write-up of your results/findings. The hiring team will not replicate your results; they probably won’t even look at it. You’re wrong if you think people will spend the time debugging your code, making sure the directories are correct, all the packages are installed to run, etc. Again, you’re not going to send an executive a text file saying “make sure to install the following packages to review my findings.” So don’t do that in your data task.

The people looking at your assignments are interested in two things: clean code and clear storytelling ability —so make sure to demonstrate that in your assignments!

Examples of a junior data scientist’s take-home assignment

Now, I’m going to describe two database schemas that are similar to what might be given to you. I’ll discuss:

• potential questions you could be asked to answer using the data or questions you should consider answering if the prompt is vague,
• how you should approach answering those questions, and
• potential “Easter eggs” and things to look out for and consider while doing your data cleaning and analysis.

But first, let’s quickly discuss the two files to send once you’ve completed the data assignment: a text-file with your code, and a PDF of your analysis and results:

The text file you’ll send back

For the text-file, keep it simple, clean, and well-documented. Format it like the example for the personal project code ; see the screenshot below for a reminder:

The analysis PDF you’ll send back

For the PDF showing your analysis, keep it organized and easy for whoever is reviewing it to know which question you are trying to answer. See the screenshot below for an example of how to organize and format this page:

Mock Data Assignment #1

Alright, alright, alright. Now I’m going to walk you through the two mock data assignments. I’ll describe a dataset and discuss a few things to consider.

Let’s start with the first one.

(Note: there is no actual data in this section, just a conceptual exercise)

In most cases, the dataset(s) you are given require at least a little bit of data cleaning. This can be anything from converting variable types, dealing with unnecessary whitespaces, things like that. Not to make you paranoid, but it appears to be relatively common for companies to have a few “Easter eggs” in the data—either in the data cleaning stage or the analysis stage of the assignment. So, it is important to keep this in mind and be on the lookout for things like null or missing values, incorrectly spelled data, and certain IDs acting like “bots” that will screw with your results. When you find these types of things, make sure to document them either in your code comments or in your write-up.

Typical questions

Oftentimes, companies will ask super general questions in the data assignment to gauge what insights you can gather from the data; questions like:

• What trends do you see in the data?
• What insights from the data are actionable?
• If there are multiple datasets, how do the datasets differ?
• What are the most interesting findings?
• Can you use a model to predict X, Y, or Z?

Great answers

After cleaning and checking the above dataset for any errors, there are a number of “low-hanging fruit” visualizations you can put together:

• Bar chart showing number of customers by acquisition channel
• Histogram showing length of time it takes users to go from installing the application to creating an account. (The thinking here is that the user experience is like a funnel—and users go from acquisition channel to installation to account creation—a long installation to account creation time suggests that there is confusion or a pain-point for users.) Similarly, you can make a funnel showing what percent of users go from acquisition channel to installation to account creation
• Plot showing number of actions by day. You can also divide users into cohorts based on when they installed the application. Do different cohorts tend to behave differently?

Obviously, this list isn’t exhaustive. But it should give you a good idea of some simple visualizations you can put together to show companies that you can clean data and present it in an aesthetically-pleasing way.

You can also suggest using the data for prediction. For example, based on when and what action a user takes, can you predict when and what their next action will be?

And lastly—even if they don’t ask, it is also good practice to have a short section at the end with any questions or concerns you have about the data and possible avenues for further analysis. You want to make the most out of the opportunity to showcase that you are vigilant and concerned about data integrity (meaning, you make sure the data is correct/accurate/error-free), and you are thinking about other ways to analyze and glean insights from the data.

Mock Data Assignment #2

Note: there is no actual data in this section, just a conceptual exercise

Similar to the mock data assignment #1, here are a few visualization ideas:

• Histogram showing lengths of time users tend to stay on a page—what about segmenting this by content_genre or content_length?
• Bubble chart showing most popular daily content
• Donut chart showing site activity by device type

Can you cluster or segment users based on what content they consume? In addition, if this was a publishing platform, can you tag a user as an “entertainment” or “sports” reader? Does the length of the page title have any effect on how “popular” content is? Does it get more visits if the title is longer/shorter?

Try to push yourself to come up with at least one visualization that you know other applicants will most likely not have—you want to stand out and demonstrate your critical thinking and creativity. You want whoever is reviewing your assignment to think to themselves, “Huh, that’s pretty cool.”

Go on to the next episode!

So this is all you have to know about the application process for a data science position. We covered: where can you look for job postings? We talked about following up after submitting your application. You got some advice to prepare for the phone screen with the recruiter. And we also reviewed how to win the take-home assignment.

If you passed these rounds, the hiring process is not over. It’s time for the onsite interviews. And as of that, in the next article I’ll show you the most common junior data science job interview questions — and also how to answer them! Here.

• If you want to learn more about how to become a data scientist, take Tomi Mester’s 50-minute video course: How to Become a Data Scientist.  (It’s free!)
• Also check out the 6-week online course: The Junior Data Scientist’s First Month video course.

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Take-Home Coding Challenge Examples That Will Change the Caliber of Your Applicants

Coding tests greatly increase the quality of applicants who advance into the final stages of technical hiring. They effectively distinguish talented candidates from unqualified applicants early on in the interview process. Coding tests also reveal a lot about candidates’ working style, efficiency, and communication skills. Take a look below at several take-home coding challenge examples that you can use in your next technical interview. 

Take-Home Interview Advantages

Technical interviews usually begin with screening candidates and selecting those who possess the required qualifications on their resumes. Then, candidates go through screening questions that plumb the depth of their skills and experience. Some of these questions might include:

• What relevant technical qualifications and training certifications do you have?
• Do you have experience using...?
• How do you handle criticism or feedback from teammates?
• How would you explain [a technical concept] to a non-tech co-worker? Do you prefer to work independently on a technical problem or figure it out with the help of others?

‍ Such questions probe candidates' deeper understanding of their subject area and interpersonal skills. After this stage, the candidate proceeds to the technical assessment portion, where they solve a challenge live or on record in their own time. Take-home interviews are particularly useful for candidates who are not comfortable with a live test. Likewise, take-home interviews allow engineers and team leaders to review a candidate’s work in detail.

Take-Home Coding Challenge Examples

Take-home challenges depend on the position level in which candidates are applying. Here are a few examples:

Data Structures

• Array: Find a pair with the given sum in an integer array using sorting or hashing.
• Trees: Write a function that deletes a given key within a binary search tree.
• Matrix: Find the shortest move sequence of a knight on a chessboard from its starting position to a destination square. 
• Linked List: Using two lists containing increasing numbers, write a function that merges them into one with numbers in the proper increasing order
• Dynamic Programming: Write a function that divides a given set of integers into two partitions with equal sum

Algorithm Questions

• Implement the Quicksort partitioning algorithm in your preferred language
• Design an algorithm that inserts a node in a sorted list/linked list
• Write an algorithm that deletes a node in a singly linked list
• Write an algorithm that interchanges two numbers in Java without a temporary variable  
• Design an algorithm that counts the number of leaf nodes in a given binary tree 

Web Development

• Build a sample website for your favorite TV show
• Recreate the layout of our flagship product page
• Build a web page with social widgets
• Design a sample mobile app for one of our services using jQuery Mobile 
• Design a portfolio website using your preferred language

Software Engineering 

• Design an app that tracks system bugs
• Develop a sentiment analysis system for a product's rating and reviews
• Design a website chatbot for answering basic queries
• Develop a mobile checkout app with QR code
• Design a web app for scheduling employee shifts

Data Science

• Create a time series forecasting model that predict store sales using historical sales data
• Build a credit score prediction model using a credit scoring algorithm
• Estimate the future price of a product using market trend analysis
• Predict the sale price of a house using given datasets

After the Test

Team leaders and engineers can build on the take-home challenge by inviting the candidate to a live interview after the challenge. This is where the applicant and interviewer go over the code together and discuss important aspects of the test, such as the candidate’s approach to problem solving. Team leaders can give feedback on the output and even code with the candidate in real-time to see how they collaborate with teammates.

Take-home coding challenge examples focus the hiring process on skill and technical expertise. It gives candidates ample time to think and accomplish what they want to do, especially if they are not comfortable being put on the spot with a live test. It also gives team leaders and interviewers a chance to examine the output in detail and make better-informed decisions regarding candidate selection. Lastly, senior engineers and team leaders can build on the take-home coding challenge by providing feedback, asking about candidates’ methodology, and doing a coding exercise together. 

Hire High-Quality Tech Talent with Filtered

Filtered clarifies the technical hiring conversation with skill-based assessment. Our platform offers a range of take-home coding challenge examples covering full-stack, SQL, data science, DevOps, and more. Our technical tests support 30+ languages and are designed to cover multiple skill levels within major engineering disciplines. What’s more, our platform is equipped with robust fraud detection so that you can hire technical talent anywhere in the world with confidence.

Filtered is a leader in skills-based, data-driven recruiting technology. Our end-to-end technical hiring platform enables you to spend time reviewing only the most qualified candidates, putting skills and aptitude at the forefront of your decisions. We’ll help you automate hiring while also applying objective, data-driven techniques to consistently and confidently select the right candidates. To get started, contact our team today or register for a FREE demo . 

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Practice Exams

Course Notes

Infographics

Career Guides

A selection of practice exams that will test your current data science knowledge. Identify key areas of improvement to strengthen your theoretical preparation, critical thinking, and practical problem-solving skills so you can get one step closer to realizing your professional goals.

Excel Mechanics

Imagine if you had to apply the same Excel formatting adjustment to both Sheet 1 and Sheet 2 (i.e., adjust font, adjust fill color of the sheets, add a couple of empty rows here and there) which contain thousands of rows. That would cost an unjustifiable amount of time. That is where advanced Excel skills come in handy as they optimize your data cleaning, formatting and analysis process and shortcut your way to a job well-done. Therefore, asses your Excel data manipulation skills with this free practice exam.  

Formatting Excel Spreadsheets

Did you know that more than 1 in 8 people on the planet uses Excel and that Office users typically spend a third of their time in Excel. But how many of them use the popular spreadsheet tool efficiently? Find out where you stand in your Excel skills with this free practice exam where you are a first-year investment banking analyst at one of the top-tier banks in the world. The dynamic nature of your position will test your skills in quick Excel formatting and various Excel shortcuts 

Hypothesis Testing

Whenever we need to verify the results of a test or experiment we turn to hypothesis testing. In this free practice exam you are a data analyst at an electric car manufacturer, selling vehicles in the US and Canada. Currently the company offers two car models – Apollo and SpeedX.  You will need to download a free Excel file containing the car sales of the two models over the last 3 years in order find out interesting insights and  test your skills in hypothesis testing. 

Confidence Intervals

Confidence Intervals refers to the probability of a population parameter falling between a range of certain values. In this free practice exam, you lead the research team at a portfolio management company with over $50 billion dollars in total assets under management. You are asked to compare the performance of 3 funds with similar investment strategies  and are given a table with the return of the three portfolios over the last 3 years. You will have to use the data to answer questions that will test your knowledge in confidence intervals. 

Fundamentals of Inferential Statistics

While descriptive statistics helps us describe and summarize a dataset, inferential statistics allows us to make predictions based off data. In this free practice exam, you are a data analyst at a leading statistical research company. Much of your daily work relates to understanding data structures and processes, as well as applying analytical theory to real-world problems on large and dynamic datasets. You will be given an excel dataset and will be tested on normal distribution, standardizing a dataset, the Central Limit Theorem among other inferential statistics questions.   

Fundamentals of Descriptive Statistics

Descriptive statistics helps us understand the actual characteristics of a dataset by generating summaries about data samples. The most popular types of descriptive statistics are measures of center: median, mode and mean. In this free practice exam you have been appointed as a Junior Data Analyst at a property developer company in the US, where you are asked to evaluate the renting prices in 9 key states. You will work with a free excel dataset file that contains the rental prices and houses over the last years.

Jupyter Notebook Shortcuts

In this free practice exam you are an experienced university professor in Statistics who is looking to upskill in data science and has joined the data science apartment. As on of the most popular coding environments for Python, your colleagues recommend you learn Jupyter Notebook as a beginner data scientist. Therefore, in this quick assessment exam you are going to be tested on some basic theory regarding Jupyter Notebook and some of its shortcuts which will determine how efficient you are at using the environment. 

Intro to Jupyter Notebooks

Jupyter is a free, open-source interactive web-based computational notebook. As one of the most popular coding environments for Python and R, you are inevitably  going to encounter Jupyter at some point in you data science journey, if you have not already. Therefore, in this free practice exam you are a professor of Applied Economics and Finance who is learning how to use Jupyter. You are going to be tested on the very basics of the Jupyter environment like how to set up the environment and some Jupyter keyboard shortcuts. 

Black-Scholes-Merton Model in Python

The Black Scholes formula is one of the most popular financial instruments used in the past 40 years. Derived by Fisher, Black Myron Scholes and Robert Merton in 1973, it has become the primary tool for derivative pricing. In this free practice exam, you are a finance student whose Applied Finance is approaching and is asked to perform the Black-Scholes-Merton formula in Python  by working on a dataset containing Tesla’s stock prices for the period between mid-2010 and mid-2020.  

Python for Financial Analysis

In a heavily regulated industry like fintech, simplicity and efficiency is key. Which is why Python is the preferred choice for programming language over the likes of Java or C++. In this free practice exam you are a university professor of Applied Economics and Finance, who is focused on running regressions and applying the CAPM model on the NASDAQ and The Coca-Cola Company Dataset for the period between 2016 and 2020 inclusive. Make sure to have the following packages running to complete your practice test: pandas, numpy, api, scipy, and pyplot as plt. 

Python Finance

Python has become the ideal programming language for the financial industry, as more and more hedge funds and large investment banks are adopting this general multi-purpose language to solve their quantitative problems. In this free practice exam on Python Finance, you are part of the IT team of a huge company, operating in the US stock market, where you are asked to analyze the performance of three market indices. The packages you need to have running are numpy, pandas and pyplot as plt.   

Machine Learning with KNN

KNN is a popular supervised machine learning algorithm that is used for solving both classification and regression problems. In this free practice exam, this is exactly what you are going to be asked to do, as you are required to create 2 datasets for 2 car dealerships in Jupyter Notebook, fit the models to the training data, find the set of parameters that best classify a car, construct a confusion matrix and more.

Excel Functions

The majority of data comes in spreadsheet format, making Excel the #1 tool of choice for professional data analysts. The ability to work effectively and efficiently in Excel is highly desirable for any data practitioner who is looking to bring value to a company. As a matter of fact, being proficient in Excel has become the new standard, as 82% of middle-skill jobs require competent use of the productivity software. Take this free Excel Functions practice exam and test your knowledge on removing duplicate values, transferring data from one sheet to another, rand using the VLOOKUP and SUMIF function.

Useful Tools in Excel

What Excel lacks in data visualization tools compared to Tableau, or computational power for analyzing big data compared to Python, it compensates with accessibility and flexibility. Excel allows you to quickly organize, visualize and perform mathematical functions on a set of data, without the need for any programming or statistical skills. Therefore, it is in your best interest to learn how to use the various Excel tools at your disposal. This practice exam is a good opportunity to test your excel knowledge in the text to column functions, excel macros, row manipulation and basic math formulas.

Excel Basics

Ever since its first release in 1985, Excel continues to be the most popular spreadsheet application to this day- with approximately 750 million users worldwide, thanks to its flexibility and ease of use. No matter if you are a data scientist or not, knowing how to use Excel will greatly improve and optimize your workflow. Therefore, in this free Excel Basics practice exam you are going to work with a dataset of a company in the Fast Moving Consumer Goods Sector as an aspiring data analyst and test your knowledge on basic Excel functions and shortcuts.

A/B Testing for Social Media

In this free A/B Testing for Social Media practice exam, you are an experienced data analyst who works at a new social media company called FilmIt. You are tasked with the job of increasing user engagement by applying the correct modifications to how users move on to the next video. You decide that the best approach is by conducting a A/B test in a controlled environment. Therefore, in order to successfully complete this task, you are going to be tested on statistical significance, 2 tailed-tests and choosing the success metrics.

Fundamentals of A/B Testing

A/B Testing is a powerful statistical tool used to compare the results between two versions of the same marketing asset such as a webpage or email in a controlled environment. An example of A/B testing is when Electronic Arts created a variation version of the sales page for the popular SimCity 5 simulation game, which performed 40% better than the control page. Speaking about video games, in this free practice test, you are a data analyst who is tasked with the job to conduct A/B testing for a game developer. You are going to be asked to choose the best way to perform an A/B test, identify the null hypothesis, choose the right evaluation metrics, and ultimately increase revenue through in-game ads.

Introduction to Data Science Disciplines

The term “Data Science” dates back to the 1960s, to describe the emerging field of working with large amounts of data that drives organizational growth and decision-making. While the essence has remained the same, the data science disciplines have changed a lot over the past decades thanks to rapid technological advancements. In this free introduction to data science practice exam, you will test your understanding of the modern day data science disciplines and their role within an organization.

Advanced SQL

In this free Advanced SQL practice exam you are a sophomore Business student who has decided to focus on improving your coding and analytical skills in the areas of relational database management systems. You are given an employee dataset containing information like titles, salaries, birth dates and department names, and are required to come up with the correct answers. This free SQL practice test will evaluate your knowledge on MySQL aggregate functions , DML statements (INSERT, UPDATE) and other advanced SQL queries.

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