case study interview data science

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.

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.

Next Gen Data Learning – Amplify Your Skills

Data Science Case Study Interview: Your Guide to Success

by Enterprise DNA Experts | 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 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 .

Data Science Interview Case Studies: How to Prepare and Excel

In the realm of Data Science Interviews , case studies play a crucial role in assessing a candidate's problem-solving skills and analytical mindset . To stand out and excel in these scenarios, thorough preparation is key. Here's a comprehensive guide on how to prepare and shine in data science interview case studies.

Understanding the Basics

Before delving into case studies, it's essential to have a solid grasp of fundamental data science concepts. Review key topics such as statistical analysis, machine learning algorithms, data manipulation, and data visualization. This foundational knowledge will form the basis of your approach to solving case study problems.

Deconstructing the Case Study

When presented with a case study during the interview, take a structured approach to deconstructing the problem. Begin by defining the business problem or question at hand. Break down the problem into manageable components and identify the key variables involved. This analytical framework will guide your problem-solving process.

🚀 Read more on: "Ultimate Guide: Crafting an Impressive UI/UX Design Portfolio for Success"

Utilizing Data Science Techniques

Apply your data science skills to analyze the provided data and derive meaningful insights. Utilize statistical methods, predictive modeling, and data visualization techniques to explore patterns and trends within the dataset. Clearly communicate your methodology and reasoning to demonstrate your analytical capabilities.

Problem-Solving Strategy

Develop a systematic problem-solving strategy to tackle case study challenges effectively. Start by outlining your approach and assumptions before proceeding to data analysis and interpretation. Implement a logical and structured process to arrive at well-supported conclusions.

Practice Makes Perfect

Engage in regular practice sessions with mock case studies to hone your problem-solving skills. Participate in data science forums and communities to discuss case studies with peers and gain diverse perspectives. The more you practice, the more confident and proficient you will become in tackling complex data science challenges.

Communicating Your Findings

Effectively communicating your findings and insights is crucial in a data science interview case study. Present your analysis in a clear and concise manner, highlighting key takeaways and recommendations. Demonstrate your storytelling ability by structuring your presentation in a logical and engaging manner.

💡 Are you a job seeker in San Francisco? Check out these fresh jobs in your area!

Exceling in data science interview case studies requires a combination of technical proficiency, analytical thinking, and effective communication . By mastering the art of case study preparation and problem-solving, you can showcase your data science skills and secure coveted job opportunities in the field.

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Top 10 Data Science Case Study Interview Questions for 2024

Data Science Case Study Interview Questions and Answers to Crack Your next Data Science Interview.

According to Harvard business review, data scientist jobs have been termed “The Sexist job of the 21st century” by Harvard business review . Data science has gained widespread importance due to the availability of data in abundance. As per the below statistics, worldwide data is expected to reach 181 zettabytes by 2025

Source: statists 2021

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“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.” — Clive Humby, 2006

Table of Contents

What is a data science case study, why are data scientists tested on case study-based interview questions, research about the company, ask questions, discuss assumptions and hypothesis, explaining the data science workflow, 10 data science case study interview questions and answers.

A data science case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. A data science case study is a real-world business problem that you would have worked on as a data scientist to build a machine learning or deep learning algorithm and programs to construct an optimal solution to your business problem.This would be a portfolio project for aspiring data professionals where they would have to spend at least 10-16 weeks solving real-world data science problems. Data science use cases can be found in almost every industry out there e-commerce , music streaming, stock market,.etc. The possibilities are endless. 

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

A case study evaluation allows the interviewer to understand your thought process. Questions on case studies can be open-ended; hence you should be flexible enough to accept and appreciate approaches you might not have taken to solve the business problem. All interviews are different, but the below framework is applicable for most data science interviews. It can be a good starting point that will allow you to make a solid first impression in your next data science job interview. In a data science interview, you are expected to explain your data science project lifecycle , and you must choose an approach that would broadly cover all the data science lifecycle activities. The below seven steps would help you get started in the right direction. 

Source: mindsbs

Business Understanding — Explain the business problem and the objectives for the problem you solved.

Data Mining — How did you scrape the required data ? Here you can talk about the connections(can be database connections like oracle, SAP…etc.) you set up to source your data.

Data Cleaning — Explaining the data inconsistencies and how did you handle them.

Data Exploration — Talk about the exploratory data analysis you performed for the initial investigation of your data to spot patterns and anomalies.

Feature Engineering — Talk about the approach you took to select the essential features and how you derived new ones by adding more meaning to the dataset flow.

Predictive Modeling — Explain the machine learning model you trained, how did you finalized your machine learning algorithm, and talk about the evaluation techniques you performed on your accuracy score.

Data Visualization — Communicate the findings through visualization and what feedback you received.

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How to Answer Case Study-Based Data Science Interview Questions?

During the interview, you can also be asked to solve and explain open-ended, real-world case studies. This case study can be relevant to the organization you are interviewing for. The key to answering this is to have a well-defined framework in your mind that you can implement in any case study, and we uncover that framework here.

Ensure that you read about the company and its work on its official website before appearing for the data science job interview . Also, research the position you are interviewing for and understand the JD (Job description). Read about the domain and businesses they are associated with. This will give you a good idea of what questions to expect.

As case study interviews are usually open-ended, you can solve the problem in many ways. A general mistake is jumping to the answer straight away.

Try to understand the context of the business case and the key objective. Uncover the details kept intentionally hidden by the interviewer. Here is a list of questions you might ask if you are being interviewed for a financial institution -

Does the dataset include all transactions from Bank or transactions from some specific department like loans, insurance, etc.?

Is the customer data provided pre-processed, or do I need to run a statistical test to check data quality?

Which segment of borrower’s your business is targeting/focusing on? Which parameter can be used to avoid biases during loan dispersion?

Make informed or well-thought assumptions to simplify the problem. Talk about your assumption with the interviewer and explain why you would want to make such an assumption. Try to narrow down to key objectives which you can solve. Here is a list of a few instances — 

As car sales increase consistently over time with no significant spikes, I assume seasonal changes do not impact your car sales. Hence I would prefer the modeling excluding the seasonality component.

As confirmed by you, the incoming data does not require any preprocessing. Hence I will skip the part of running statistical tests to check data quality and perform feature selection.

As IoT devices are capturing temperature data at every minute, I am required to predict weather daily. I would prefer averaging out the minute data to a day to have data daily.

Get Closer To Your Dream of Becoming a Data Scientist with 150+ Solved End-to-End ML Projects

Now that you have a clear and focused objective to solve the business case. You can start leveraging the 7-step framework we briefed upon above. Think of the mining and cleaning activities that you are required to perform. Talk about feature selection and why you would prefer some features over others, and lastly, how you would select the right machine learning model for the business problem. Here is an example for car purchase prediction from auctions -

First, Prepare the relevant data by accessing the data available from various auctions. I will selectively choose the data from those auctions which are completed. At the same time, when selecting the data, I need to ensure that the data is not imbalanced.

Now I will implement feature engineering and selection to create and select relevant features like a car manufacturer, year of purchase, automatic or manual transmission…etc. I will continue this process if the results are not good on the test set.

Since this is a classification problem, I will check the prediction using the Decision trees and Random forest as this algorithm tends to do better for classification problems. If the results score is unsatisfactory, I can perform hyper parameterization to fine-tune the model and achieve better accuracy scores.

In the end, summarise the answer and explain how your solution is best suited for this business case. How the team can leverage this solution to gain more customers. For instance, building on the car sales prediction analogy, your response can be

For the car predicted as a good car during an auction, the dealers can purchase those cars and minimize the overall losses they incur upon buying a bad car. 

Often, the company you are being interviewed for would select case study questions based on a business problem they are trying to solve or have already solved. Here we list down a few case study-based data science interview questions and the approach to answering those in the interviews. Note that these case studies are often open-ended, so there is no one specific way to approach the problem statement.

1. How would you improve the bank's existing state-of-the-art credit scoring of borrowers? How will you predict someone can face financial distress in the next couple of years?

Consider the interviewer has given you access to the dataset. As explained earlier, you can think of taking the following approach. 

Ask Questions — 

Q: What parameter does the bank consider the borrowers while calculating the credit scores? Do these parameters vary among borrowers of different categories based on age group, income level, etc.?

Q: How do you define financial distress? What features are taken into consideration?

Q: Banks can lend different types of loans like car loans, personal loans, bike loans, etc.  Do you want me to focus on any one loan category?

Discuss the Assumptions  — 

As debt ratio is proportional to monthly income, we assume that people with a high debt ratio(i.e., their loan value is much higher than the monthly income) will be an outlier.

Monthly income tends to vary (mainly on the upside) over two years. Cases, where the monthly income is constant can be considered data entry issues and should not be considered for analysis. I will choose the regression model to fill up the missing values.

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Building end-to-end Data Science Workflows — 

Firstly, I will carefully select the relevant data for my analysis. I will deselect records with insane values like people with high debt ratios or inconsistent monthly income.

Identifying essential features and ensuring they do not contain missing values. If they do, fill them up. For instance, Age seems to be a necessary feature for accepting or denying a mortgage. Also, ensuring data is not imbalanced as a meager percentage of borrowers will be defaulter when compared to the complete dataset.

As this is a binary classification problem, I will start with logistic regression and slowly progress towards complex models like decision trees and random forests.

Conclude — 

Banks play a crucial role in country economies. They decide who can get finance and on what terms and can make or break investment decisions. Individuals and companies need access to credit for markets and society to function.

You can leverage this credit scoring algorithm to determine whether or not a loan should be granted by predicting the probability that somebody will experience financial distress in the next two years.

2. At an e-commerce platform, how would you classify fruits and vegetables from the image data?

Q: Do the images in the dataset contain multiple fruits and vegetables, or would each image have a single fruit or a vegetable?

Q: Can you help me understand the number of estimated classes for this classification problem?

Q: What would be an ideal dimension of an image? Do the images vary within the dataset? Are these color images or grey images?

Upon asking the above questions, let us assume the interviewer confirms that each image would contain either one fruit or one vegetable. Hence there won't be multiple classes in a single image, and our website has roughly 100 different varieties of fruits and vegetables. For simplicity, the dataset contains 50,000 images each the dimensions are 100 X 100 pixels.

Assumptions and Preprocessing—

I need to evaluate the training and testing sets. Hence I will check for any imbalance within the dataset. The number of training images for each class should be consistent. So, if there are n number of images for class A, then class B should also have n number of training images (or a variance of 5 to 10 %). Hence if we have 100 classes, the number of training images under each class should be consistent. The dataset contains 50,000 images average image per class is close to 500 images.

I will then divide the training and testing sets into 80: 20 ratios (or 70:30, whichever suits you best). I assume that the images provided might not cover all possible angles of fruits and vegetables; hence such a dataset can cause overfitting issues once the training gets completed. I will keep techniques like Data augmentation handy in case I face overfitting issues while training the model.

End to End Data Science Workflow — 

As this is a larger dataset, I would first check the availability of GPUs as processing 50,000 images would require high computation. I will use the Cuda library to move the training set to GPU for training.

I choose to develop a convolution neural network (CNN) as these networks tend to extract better features from the images when compared to the feed-forward neural network. Feature extraction is quite essential while building the deep neural network. Also, CNN requires way less computation requirement when compared to the feed-forward neural networks.

I will also consider techniques like Batch normalization and learning rate scheduling to improve the accuracy of the model and improve the overall performance of the model. If I face the overfitting issue on the validation set, I will choose techniques like dropout and color normalization to over those.

Once the model is trained, I will test it on sample test images to see its behavior. It is quite common to model that doing well on training sets does not perform well on test sets. Hence, testing the test set model is an important part of the evaluation.

The fruit classification model can be helpful to the e-commerce industry as this would help them classify the images and tag the fruit and vegetables belonging to their category.The fruit and vegetable processing industries can use the model to organize the fruits to the correct categories and accordingly instruct the device to place them on the cover belts involved in packaging and shipping to customers.

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3. How would you determine whether Netflix focuses more on TV shows or Movies?

Q: Should I include animation series and movies while doing this analysis?

Q: What is the business objective? Do you want me to analyze a particular genre like action, thriller, etc.?

Q: What is the targeted audience? Is this focus on children below a certain age or for adults?

Let us assume the interview responds by confirming that you must perform the analysis on both movies and animation data. The business intends to perform this analysis over all the genres, and the targeted audience includes both adults and children.

Assumptions — 

It would be convenient to do this analysis over geographies. As US and India are the highest content generator globally, I would prefer to restrict the initial analysis over these countries. Once the initial hypothesis is established, you can scale the model to other countries.

While analyzing movies in India, understanding the movie release over other months can be an important metric. For example, there tend to be many releases in and around the holiday season (Diwali and Christmas) around November and December which should be considered. 

End to End  Data Science Workflow — 

Firstly, we need to select only the relevant data related to movies and TV shows among the entire dataset. I would also need to ensure the completeness of the data like this has a relevant year of release, month-wise release data, Country-wise data, etc.

After preprocessing the dataset, I will do feature engineering to select the data for only those countries/geographies I am interested in. Now you can perform EDA to understand the correlation of Movies and TV shows with ratings, Categories (drama, comedies…etc.), actors…etc.

Lastly, I would focus on Recommendation clicks and revenues to understand which of the two generate the most revenues. The company would likely prefer the categories generating the highest revenue ( TV Shows vs. Movies) over others.

This analysis would help the company invest in the right venture and generate more revenue based on their customer preference. This analysis would also help understand the best or preferred categories, time in the year to release, movie directors, and actors that their customers would like to see.

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4. How would you detect fake news on social media?

Q: When you say social media, does it mean all the apps available on the internet like Facebook, Instagram, Twitter, YouTub, etc.?

Q: Does the analysis include news titles? Does the news description carry significance?

Q: As these platforms contain content from multiple languages? Should the analysis be multilingual?

Let us assume the interviewer responds by confirming that the news feeds are available only from Facebook. The new title and the news details are available in the same block and are not segregated. For simplicity, we would prefer to categorize the news available in the English language.

Assumptions and Data Preprocessing — 

I would first prefer to segregate the news title and description. The news title usually contains the key phrases and the intent behind the news. Also, it would be better to process news titles as that would require low computing than processing the whole text as a data scientist. This will lead to an efficient solution.

Also, I would also check for data imbalance. An imbalanced dataset can cause the model to be biased to a particular class. 

I would also like to take a subset of news that may focus on a specific category like sports, finance , etc. Gradually, I will increase the model scope, and this news subset would help me set up my baseline model, which can be tweaked later based on the requirement.

Firstly, it would be essential to select the data based on the chosen category. I take up sports as a category I want to start my analysis with.

I will first clean the dataset by checking for null records. Once this check is done, data formatting is required before you can feed to a natural network. I will write a function to remove characters like !”#$%&’()*+,-./:;<=>?@[]^_`{|}~ as their character does not add any value for deep neural network learning. I will also implement stopwords to remove words like ‘and’, ‘is”, etc. from the vocabulary. 

Then I will employ the NLP techniques like Bag of words or TFIDF based on the significance. The bag of words can be faster, but TF IDF can be more accurate and slower. Selecting the technique would also depend upon the business inputs.

I will now split the data in training and testing, train a machine learning model, and check the performance. Since the data set is heavy on text models like naive bayes tends to perform better in these situations.

Conclude  — 

Social media and news outlets publish fake news to increase readership or as part of psychological warfare. In general, the goal is profiting through clickbait. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues. The trained model will help curb such news and add value to the reader's time.

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5. How would you forecast the price of a nifty 50 stock?

Q: Do you want me to forecast the nifty 50 indexes/tracker or stock price of a specific stock within nifty 50?

Q: What do you want me to forecast? Is it the opening price, closing price, VWAP, highest of the day, etc.?

Q: Do you want me to forecast daily prices /weekly/monthly prices?

Q: Can you tell me more about the historical data available? Do we have ten years or 15 years of recorded data?

With all these questions asked to the interviewer, let us assume the interviewer responds by saying that you should pick one stock among nifty 50 stocks and forecast their average price daily. The company has historical data for the last 20 years.

Assumptions and Data preprocessing — 

As we forecast the average price daily, I would consider VWAP my target or predictor value. VWAP stands for Volume Weighted Average Price, and it is a ratio of the cumulative share price to the cumulative volume traded over a given time.

Solving this data science case study requires tracking the average price over a period, and it is a classical time series problem. Hence I would refrain from using the classical regression model on the time series data as we have a separate set of machine learning models (like ARIMA , AUTO ARIMA, SARIMA…etc.) to work with such datasets.

Like any other dataset, I will first check for null and understand the % of null values. If they are significantly less, I would prefer to drop those records.

Now I will perform the exploratory data analysis to understand the average price variation from the last 20 years. This would also help me understand the tread and seasonality component of the time series data. Alternatively, I will use techniques like the Dickey-Fuller test to know if the time series is stationary or not. 

Usually, such time series is not stationary, and then I can now decompose the time series to understand the additive or multiplicative nature of time series. Now I can use the existing techniques like differencing, rolling stats, or transformation to make the time series non-stationary.

Lastly, once the time series is non-stationary, I will separate train and test data based on the dates and implement techniques like ARIMA or Facebook prophet to train the machine learning model .

Some of the major applications of such time series prediction can occur in stocks and financial trading, analyzing online and offline retail sales, and medical records such as heart rate, EKG, MRI, and ECG.

Time series datasets invoke a lot of enthusiasm between data scientists . They are many different ways to approach a Time series problem, and the process mentioned above is only one of the know techniques.

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6. How would you forecast the weekly sales of Walmart? Which department impacted most during the holidays?

Q: Walmart usually operates three different stores - supermarkets, discount stores, and neighborhood stores. Which store data shall I pick to get started with my analysis? Are the sales tracked in US dollars?

Q: How would I identify holidays in the historical data provided? Is the store closed on Black Friday week, super bowl week, or Christmas week?

Q: What are the evaluation or the loss criteria? How many departments are present across all store types?

Let us assume the interviewer responds by saying you must forecast weekly sales department-wise and not store type-wise in US dollars. You would be provided with a flag within the dataset to inform weeks having holidays. There are over 80 departments across three types of stores.

As we predict the weekly sales, I would assume weekly sales to be the target or the predictor for our data model before training.

We are tracking sales price weekly, We will use a regression model to predict our target variable, “Weekly_Sales,” a grouped/hierarchical time series. We will explore the following categories of models, engineer features, and hyper-tune parameters to choose a model with the best fit.

- Linear models

- Tree models

- Ensemble models

I will consider MEA, RMSE, and R2 as evaluation criteria.

End to End Data Science Workflow-

The foremost step is to figure out essential features within the dataset. I would explore store information regarding their size, type, and the total number of stores present within the historical dataset.

The next step would be to perform feature engineering; as we have weekly sales data available, I would prefer to extract features like ‘WeekofYear’, ‘Month’, ‘Year’, and ‘Day’. This would help the model to learn general trends.

Now I will create store and dept rank features as this is one of the end goals of the given problem. I would create these features by calculating the average weekly sales.

Now I will perform the exploratory data analysis (a.k.a EDA) to understand what story does the data has to say? I will analyze the stores and weekly dept sales for the historical data to foresee the seasonality and trends. Weekly sales against the store and weekly sales against the department to understand their significance and whether these features must be retained that will be passed to the machine learning models.

After feature engineering and selection, I will set up a baseline model and run the evaluation considering MAE, RMSE and R2. As this is a regression problem, I will begin with simple models like linear regression and SGD regressor. Later, I will move towards complex models, like Decision Trees Regressor, if the need arises. LGBM Regressor and SGB regressor.

Sales forecasting can play a significant role in the company’s success. Accurate sales forecasts allow salespeople and business leaders to make smarter decisions when setting goals, hiring, budgeting, prospecting, and other revenue-impacting factors. The solution mentioned above is one of the many ways to approach this problem statement.

With this, we come to the end of the post. But let us do a quick summary of the techniques we learned and how they can be implemented. We would also like to provide you with some practice case studies questions to help you build up your thought process for the interview.

7. Considering an organization has a high attrition rate, how would you predict if an employee is likely to leave the organization?

8. How would you identify the best cities and countries for startups in the world?

9. How would you estimate the impact on Air Quality across geographies during Covid 19?

10. A Company often faces machine failures at its factory. How would you develop a model for predictive maintenance?

Do not get intimated by the problem statement; focus on your approach -

Ask questions to get clarity

Discuss assumptions, don't assume things. Let the data tell the story or get it verified by the interviewer.

Build Workflows — Take a few minutes to put together your thoughts; start with a more straightforward approach.

Conclude — Summarize your answer and explain how it best suits the use case provided.

We hope these case study-based data scientist interview questions will give you more confidence to crack your next data science interview.

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ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

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Data science case study interview

Many accomplished students and newly minted AI professionals ask us$:$ How can I prepare for interviews? Good recruiters try setting up job applicants for success in interviews, but it may not be obvious how to prepare for them. We interviewed over 100 leaders in machine learning and data science to understand what AI interviews are and how to prepare for them.

TABLE OF CONTENTS

• I What to expect in the data science case study interview
• II Recommended framework
• III Interview tips
• IV Resources

AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. The data science case study interview focuses on technical and decision making skills, and you’ll encounter it during an onsite round for a Data Scientist (DS), Data Analyst (DA), Machine Learning Engineer (MLE) or Machine Learning Researcher (MLR). You can learn more about these roles in our AI Career Pathways report and about other types of interviews in The Skills Boost .

I   What to expect in the data science case study interview

The interviewer is evaluating your approach to a real-world data science problem. The interview revolves around a technical question which can be open-ended. There is no exact solution to the question; it’s your thought process that the interviewer is evaluating. Here’s a list of interview questions you might be asked:

• How many cashiers should be at a Walmart store at a given time?
• You notice a spike in the number of user-uploaded videos on your platform in June. What do you think is the cause, and how would you test it?
• Your company is thinking of changing its logo. Is it a good idea? How would you test it?
• Could you tell if a coin is biased?
• In a given day, how many birthday posts occur on Facebook?
• What are the different performance metrics for evaluating ride sharing services?
• How will you test if a chosen credit scoring model works or not? What dataset(s) do you need?
• Given a user’s history of purchases, how do you predict their next purchase?

II   Recommended framework

All interviews are different, but the ASPER framework is applicable to a variety of case studies:

• Ask . Ask questions to uncover details that were kept hidden by the interviewer. Specifically, you want to answer the following questions: “what are the product requirements and evaluation metrics?”, “what data do I have access to?”, ”how much time and computational resources do I have to run experiments?”.
• Suppose . Make justified assumptions to simplify the problem. Examples of assumptions are: “we are in small data regime”, “events are independent”, “the statistical significance level is 5%”, “the data distribution won’t change over time”, “we have three weeks”, etc.
• Plan . Break down the problem into tasks. A common task sequence in the data science case study interview is: (i) data engineering, (ii) modeling, and (iii) business analysis.
• Execute . Announce your plan, and tackle the tasks one by one. In this step, the interviewer might ask you to write code or explain the maths behind your proposed method.
• Recap . At the end of the interview, summarize your answer and mention the tools and frameworks you would use to perform the work. It is also a good time to express your ideas on how the problem can be extended.

III   Interview tips

Every interview is an opportunity to show your skills and motivation for the role. Thus, it is important to prepare in advance. Here are useful rules of thumb to follow:

Articulate your thoughts in a compelling narrative.

Data scientists often need to convert data into actionable business insights, create presentations, and convince business leaders. Thus, their communication skills are evaluated in interviews and can be the reason of a rejection. Your interviewer will judge the clarity of your thought process, your scientific rigor, and how comfortable you are using technical vocabulary.

Example 1: Your interviewer will notice if you say “correlation matrix” when you actually meant “covariance matrix”.

Example 2: Mispronouncing a widely used technical word or acronym such as Poisson, ICA, or AUC can affect your credibility. For instance, ICA is pronounced aɪ-siː-eɪ (i.e., “I see A”) rather than “Ika”.

Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Tie your task to the business logic.

Example 1: If you are asked to improve Instagram’s news feed, identify what’s the goal of the product. Is it to have users spend more time on the app, users click on more ads, or drive interactions between users?

Example 2: You present graphs to show the number of salesperson needed in a retail store at a given time. It is a good idea to also discuss the savings your insight can lead to.

Alternatively, your interviewer might give you the business goal, such as improving retention, engagement or reducing employee churn, but expect you to come up with a metric to optimize.

Example: If the goal is to improve user engagement, you might use daily active users as a proxy and track it using their clicks (shares, likes, etc.).

Brush up your data science foundations before the interview.

You have to leverage concepts from probability and statistics such as correlation vs. causation or statistical significance. You should also be able to read a test table.

Example: You’re a professor currently evaluating students with a final exam, but considering switching to a project-based evaluation. A rumor says that the majority of your students are opposed to the switch. Before making the switch, what would you like to test? In this question, you should introduce notation to state your hypothesis and leverage tools such as confidence intervals, p-values, distributions, and tables. Your interviewer might then give you more information. For instance, you have polled a random sample of 300 students in your class and observed that 60% of them were against the switch.

Avoid clear-cut statements.

Because case studies are often open-ended and can have multiple valid solutions, avoid making categorical statements such as “the correct approach is …” You might offend the interviewer if the approach they are using is different from what you describe. It’s also better to show your flexibility with and understanding of the pros and cons of different approaches.

Study topics relevant to the company.

Data science case studies are often inspired by in-house projects. If the team is working on a domain-specific application, explore the literature.

Example 1: If the team is working on time series forecasting, you can expect questions about ARIMA, and follow-ups on how to test whether a coefficient of your model should be zero.

Example 2: If the team is building a recommender system, you might want to read about the types of recommender systems such as collaborative filtering or content-based recommendation. You may also learn about evaluation metrics for recommender systems ( Shani and Gunawardana, 2017 ).

Listen to the hints given by your interviewer.

Example: The interviewer gives you a spreadsheet in which one of the columns has more than 20% missing values, and asks you what you would do about it. You say that you’d discard incomplete records. Your interviewer follows up with “Does the dataset size matter?”. In this scenario, the interviewer expects you to request more information about the dataset and adapt your answer. For instance, if the dataset is small, you might want to replace the missing values with a good estimate (such as the mean of the variable).

Show your motivation.

In data science case study interviews, the interviewer will evaluate your excitement for the company’s product. Make sure to show your curiosity, creativity and enthusiasm.

When you are not sure of your answer, be honest and say so.

Interviewers value honesty and penalize bluffing far more than lack of knowledge.

When out of ideas or stuck, think out loud rather than staying silent.

Talking through your thought process will help the interviewer correct you and point you in the right direction.

IV   Resources

You can build decision making skills by reading data science war stories and exposing yourself to projects . Here’s a list of useful resources to prepare for the data science case study interview.

• In Your Client Engagement Program Isn’t Doing What You Think It Is , Stitch Fix scientists (Glynn and Prabhakar) argue that “optimal” client engagement tactics change over time and companies must be fluid and adaptable to accommodate ever-changing client needs and business strategies. They present a contextual bandit framework to personalize an engagement strategy for each individual client.
• For many Airbnb prospective guests, planning a trip starts at the search engine. Search Engine Optimization (SEO) helps make Airbnb painless to find for past guests and easy to discover for new ones. In Experimentation & Measurement for Search Engine Optimization , Airbnb data scientist De Luna explains how you can measure the effectiveness of product changes in terms of search engine rankings.
• Coordinating ad campaigns to acquire new users at scale is time-consuming, leading Lyft’s growth team to take on the challenge of automation. In Building Lyft’s Marketing Automation Platform , Sampat shares how Lyft uses algorithms to make thousands of marketing decisions each day such as choosing bids, budgets, creatives, incentives, and audiences; running tests; and more.
• In this Flower Species Identification Case Study , Olson goes over a basic Python data analysis pipeline from start to finish to illustrate what a typical data science workflow looks like.
• Before producing a movie, producers and executives are tasked with critical decisions such as: do we shoot in Georgia or in Gibraltar? Do we keep a 10-hour workday or a 12-hour workday? In Data Science and the Art of Producing Entertainment at Netflix , Netflix scientists and engineers (Kumar et al.) show how data science can help answer these questions and transform a century-old industry with data science.

• Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at deeplearning.ai

Acknowledgment(s)

• The layout for this article was originally designed and implemented by Jingru Guo , Daniel Kunin , and Kian Katanforoosh for the deeplearning.ai AI Notes , and inspired by Distill .

Footnote(s)

• Job applicants are subject to anywhere from 3 to 8 interviews depending on the company, team, and role. You can learn more about the types of AI interviews in The Skills Boost . This includes the machine learning algorithms interview , the deep learning algorithms interview , the machine learning case study interview , the deep learning case study interview , the data science case study interview , and more coming soon.
• It takes time and effort to acquire acumen in a particular domain. You can develop your acumen by regularly reading research papers, articles, and tutorials. Twitter, Medium, and websites of data science and machine learning conferences (e.g., KDD, NeurIPS, ICML, and the like) are good places to read the latest releases. You can also find a list of hundreds of Stanford students' projects on the Stanford CS230 website .

To reference this article, please use:

Workera, "Data Science Case Study Interview".

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Data Science Interview Practice: Machine Learning Case Study

A common interview type for data scientists and machine learning engineers is the machine learning case study. In it, the interviewer will ask a question about how the candidate would build a certain model. These questions can be challenging for new data scientists because the interview is open-ended and new data scientists often lack practical experience building and shipping product-quality models.

I have a lot of practice with these types of interviews as a result of my time at Insight , my many experiences interviewing for jobs , and my role in designing and implementing Intuit’s data science interview. Similar to my last article where I put together an example data manipulation interview practice problem , this time I will walk through a practice case study and how I would work through it.

My Approach

Case study interviews are just conversations. This can make them tougher than they need to be for junior data scientists because they lack the obvious structure of a coding interview or data manipulation interview . I find it’s helpful to impose my own structure on the conversation by approaching it in this order:

• Problem : Dive in with the interviewer and explore what the problem is. Look for edge cases or simple and high-impact parts of the problem that you might be able to close out quickly.
• Metrics : Once you have determined the scope and parameters of the problem you’re trying to solve, figure out how you will measure success. Focus on what is important to the business and not just what is easy to measure.
• Data : Figure out what data is available to solve the problem. The interviewer might give you a couple of examples, but ask about additional information sources. If you know of some public data that might be useful, bring it up here too.
• Labels and Features : Using the data sources you discussed, what features would you build? If you are attacking a supervised classification problem, how would you generate labels? How would you see if they were useful?
• Model : Now that you have a metric, data, features, and labels, what model is a good fit? Why? How would you train it? What do you need to watch out for?
• Validation : How would you make sure your model works offline? What data would you hold out to test your model works as expected? What metrics would you measure?
• Deployment and Monitoring : Having developed a model you are comfortable with, how would you deploy it? Does it need to be real-time or is it sufficient to batch inputs and periodically run the model? How would you check performance in production? How would you monitor for model drift where its performance changes over time?

Here is the prompt:

At Twitter, bad actors occasionally use automated accounts, known as “bots”, to abuse our platform. How would you build a system to help detect bot accounts?

At the start of the interview I try to fully explore the bounds of the problem, which is often open ended. My goal with this part of the interview is to:

• Understand the problem and all the edges cases.
• Come to an agreement with the interviewer on the scope—narrower is better!—of the problem to solve.
• Demonstrate any knowledge I have on the subject, especially from researching the company previously.

Our Twitter bot prompt has a lot of angles from which we could attack. I know Twitter has dozens of types of bots, ranging from my harmless Raspberry Pi bots , to “Russian Bots” trying to influence elections , to bots spreading spam . I would pick one problem to focus on using my best guess as to business impact. In this case spam bots are likely a problem that causes measurable harm (drives users away, drives advertisers away). Russian bots are probably a bigger issue in terms of public perception, but that’s much harder to measure.

After deciding on the scope, I would ask more about the systems they currently have to deal with it. Likely Twitter has an ops team to help identify spam and block accounts and they may even have a rules based system. Those systems will be a good source of data about the bad actors and they likely also have metrics they track for this problem.

Having agreed on what part of the problem to focus on, we now turn to how we are going to measure our impact. There is no point shipping a model if you can’t measure how it’s affecting the business.

Metrics and model use go hand-in-hand, so first we have to agree on what the model will be used for. For spam we could use the model to just mark suspected accounts for human review and tracking, or we could outright block accounts based on the model result. If we pick the human review option, it’s probably more important to get all the bots even if some good customers are affected. If we go with immediate action, it is likely more important to only ban truly bad accounts. I covered thinking about metrics like this in detail in another post, What Machine Learning Metric to Use . Take a look!

I would argue the automatic blocking model will have higher impact because it frees our ops people to focus on other bad behavior. We want two sets of metrics: offline for when we are training and online for when the model is deployed.

Our offline metric will be precision because, based on the argument above, we want to be really sure we’re only banning bad accounts.

Our online metrics are more business focused:

• Ops time saved : Ops is currently spending some amount of time reviewing spam; how much can we cut that down?
• Spam fraction : What percent of Tweets are spam? Can we reduce this?

It is often useful to normalize metrics, like the spam fraction metric, so they don’t go up or down just because we have more customers!

Now that we know what we’re doing and how to measure its success, it’s time to figure out what data we can use. Just based on how a company operates, you can make a really good guess as to the data they have. For Twitter we know they have to track Tweets, accounts, and logins, so they must have databases with that information. Here are what I think they contain:

• Tweets database : Sending account, mentioned accounts, parent Tweet, Tweet text.
• Interactions database : Account, Tweet, action (retweet, favorite, etc.).
• Accounts database : Account name, handle, creation date, creation device, creation IP address.
• Following database : Account, followed account.
• Login database : Account, date, login device, login IP address, success or fail reason.
• Ops database : Account, restriction, human reasoning.

And a lot more. From these we can find out a lot about an account and the Tweets they send, who they send to, who those people react to, and possibly how login events tie different accounts together.

Labels and Features

Having figured out what data is available, it’s time to process it. Because I’m treating this as a classification problem, I’ll need labels to tell me the ground truth for accounts, and I’ll need features which describe the behavior of the accounts.

Since there is an ops team handling spam, I have historical examples of bad behavior which I can use as positive labels. 1 If there aren’t enough I can use tricks to try to expand my labels, for example looking at IP address or devices that are associated with spammers and labeling other accounts with the same login characteristics.

Negative labels are harder to come by. I know Twitter has verified users who are unlikely to be spam bots, so I can use them. But verified users are certainly very different from “normal” good users because they have far more followers.

It is a safe bet that there are far more good users than spam bots, so randomly selecting accounts can be used to build a negative label set.

To build features, it helps to think about what sort of behavior a spam bot might exhibit, and then try to codify that behavior into features. For example:

• Bots can’t write truly unique messages ; they must use a template or language generator. This should lead to similar messages, so looking at how repetitive an account’s Tweets are is a good feature.
• Bots are used because they scale. They can run all the time and send messages to hundreds or thousands (or millions) or users. Number of unique Tweet recipients and number of minutes per day with a Tweet sent are likely good features.
• Bots have a controller. Someone is benefiting from the spam, and they have to control their bots. Features around logins might help here like number of accounts seen from this IP address or device, similarity of login time, etc.

Model Selection

I try to start with the simplest model that will work when starting a new project. Since this is a supervised classification problem and I have written some simple features, logistic regression or a forest are good candidates. I would likely go with a forest because they tend to “just work” and are a little less sensitive to feature processing. 2

Deep learning is not something I would use here. It’s great for image, video, audio, or NLP, but for a problem where you have a set of labels and a set of features that you believe to be predictive it is generally overkill.

One thing to consider when training is that the dataset is probably going to be wildly imbalanced. I would start by down-sampling (since we likely have millions of events), but would be ready to discuss other methods and trade offs.

Validation is not too difficult at this point. We focus on the offline metric we decided on above: precision. We don’t have to worry much about leaking data between our holdout sets if we split at the account level, although if we include bots from the same botnet into our different sets there will be a little data leakage. I would start with a simple validation/training/test split with fixed fractions of the dataset.

Since we want to classify an entire account and not a specific tweet, we don’t need to run the model in real-time when Tweets are posted. Instead we can run batches and can decide on the time between runs by looking at something like the characteristic time a spam bot takes to send out Tweets. We can add rate limiting to Tweet sending as well to slow the spam bots and give us more time to decide without impacting normal users.

For deployment, I would start in shadow mode , which I discussed in detail in another post . This would allow us to see how the model performs on real data without the risk of blocking good accounts. I would track its performance using our online metrics: spam fraction and ops time saved. I would compute these metrics twice, once using the assumption that the model blocks flagged accounts, and once assuming that it does not block flagged accounts, and then compare the two outcomes. If the comparison is favorable, the model should be promoted to action mode.

Let Me Know!

I hope this exercise has been helpful! Please reach out and let me know at @alex_gude if you have any comments or improvements!

In this case a positive label means the account is a spam bot, and a negative label means they are not.  ↩

If you use regularization with logistic regression (and you should) you need to scale your features. Random forests do not require this.  ↩

Data Science Interview Guide - Questions from 80 Different Companies

A data science interview guide that includes 900+ real interview questions from 80 different companies in 2020 and 2021

Introduction

To be called a data scientist is slowly becoming a prestigious trait; every year the pool of data scientist roles in the world expands exponentially. Back in 2012, Harvard Business Review called data scientist the sexiest job of the 21st century and the growing trend of roles in the industry seems to be confirming that statement. However, how does one pass the rigorous interview process to get a job as a data scientist ? We have done some research in this data science interview guide to find out.

The data scientist interview process can be very broad and complex. Since your role can incorporate so many areas (depending on the company you work for), the data science interview questions getting asked are quite diverse. For example, you can go to an interview and get asked question on statistics, modeling and algorithms, or questions on coding, system design and product. Due to the diverse nature of questions, we have decided to analyze them in order to help you better prepare for your future interview.

The goal of this data science interview guide is to look at a repository of real interview questions from real companies that we have collected over the years. These questions have been used to conduct an analysis of what an interview consists of at a company. We have reviewed all the applicable questions and present our findings in this article.

Description and Methodology of the Analysis

The research in this data science interview guide will identify to what degree several types of questions are being asked in data science interviews as well as the relation between companies and the types of questions being asked. Furthermore, the research in this data science interview guide will examine significant trends among companies, questions types and questions themselves, through descriptive statistics.

The data we have gathered comes from various job search boards and websites as well as company review platforms such as Glassdoor, Indeed, Reddit and Blind App. For the purpose of this research, we have collected 903 different questions over the past 4 years. The 3 most important data points we have gathered from our sources that we will use for this analysis are company name, question type and description of the question(s) asked.

The question type data in our research has been produced by sectioning questions into pre-determined categories. These categories have been produced by an expert analysis of the interview experience description taken from our sources. The categories produced are: algorithms, business case, coding, modeling, probability, product, statistics, system design and technical. We will go into more detail on each category in the section on most tested technical concepts in order to get an understanding of the categorization method.

What Kind of Questions are Being Asked on Data Science Interviews?

Our analysis of 903 different data science interview questions has shown some meaningful insights.

When we look at all the questions broken down by category, we can see some meaningful insights. Coding and modeling questions are the most dominant types of questions being asked on data science interviews, with more than half of all the questions we analyzed coming from that area; therefore, we can conclude that demonstrating practical skills is more dominant in data science interviews. Data science coding type questions are especially prominent, consisting of more than one third of all questions. This finding is no surprise considering that these are probably the two most important skills a data scientist should master before interviewing. Furthermore, we can see that theoretical question types such as algorithms and statistics are being asked to a certain extent; 24% of all questions comes from these two categories. Other categories are not as represented which is reasonable, considering the nature of such question types as well as the nature of a data scientist role.

Breaking down the questions by the company which asked them on the interview gives us more great insights for this data science interview guide. We can see that Facebook is clearly dominating the scene with over 20% of all questions coming from this company; no other company is even close to getting to 100 questions whereas Facebook is only 7 questions away from getting to a figure of 200. Furthermore, Facebook has more questions (193) than the next 4 top companies combined (190). Amazon is the second company on this list with 71 questions, and it is the only company other than Facebook with more than 50 questions. Following Amazon are companies such as Goldman Sachs, Google, IBM, and Microsoft. The conclusion from this analysis is that big tech companies are generally leading the growth in data science, with Facebook being the catalyst in terms of the number of roles they are hiring. It is important to note that not all companies from our data set have been included in this graph for ease of readership; however, all the companies excluded from the graph had values significantly lower than our outliers.

Analysis of FAANG Companies

Due to their size, innovation capabilities and industry leadership in data science as well as tech overall, we will cover Facebook, Amazon, Apple, Netflix and Google in more depth; after all, they would not get their own acronym if they have not been the drivers of change in technology.

When we break down the question categories and percentage of questions appearing from each category, and we separate the results between FAANG and non-FAANG data science companies , we can see one very clear difference: the tech giants put a lot more emphasis on coding. Eighteen percent more, to be exact. However, non-FAANG companies ask a lot more modeling questions; seventeen percent more. There are no significant variations in any of the other categories.

If we analyze Facebook separately, we can see that it follows a similar trend as when we compared FAANG vs non-FAANG companies: more coding and less modeling than average. However, Facebook also asks double the amount of product questions than the average, which makes knowledge about how their social media platforms work that much more valuable.

When we break down Amazon in the similar fashion, we can see a slightly different picture. On top of putting a high emphasis on coding as other FAANG companies, Amazon also puts a lot of emphasis on modeling (24%). Where they lack behind other FAANG members is product questions: while rest of the companies have an average of 10% of questions from this category, Amazon has none.

Due to the low number of questions, we have gathered from Apple (11), there are only 4 categories this company has questions in. It is interesting to note that even with a smaller sample, the trend towards coding emphasizing around 50% of all questions is as true for Apple as is for FAANG overall.

Google’s breakdown seems to resemble the graph on categorization of all questions, more than it resembles the questions’ breakdown of FAANG companies. We can see that they have lower number of coding, but higher number of modeling questions as compared to their FAANG peers. Furthermore, they have half the product questions and more than double business case questions. This could potentially be explained by Google’s diversity in business operations, where certain roles and organizational structures would require data scientists with a different set of skills.

Due to the low number of questions gathered from Netflix, this company is not further analyzed in this section.

Comparing FAANG vs Non-FAANG Data Science Interview Questions

Most tested technical concepts on data science interviews.

Here, in this data science interview guide, we will cover the categorization method we used to structure the questions for analysis. Furthermore, we will analyze each category in depth and offer a real image of industry requirements for data science interviews. Finally, we will go through the most tested technical concepts for each of the question type categories we used to structure our research and offer some real-world examples of those concepts.

Coding questions have been identified as all questions that require some sort of data manipulation (through code) to identify insights. For example, question asking a candidate to do SQL joins would be considered a coding question. Coding questions are designed to test the interviewee’s coding ability, problem solving skills and creativity, usually demonstrated on a computer or a whiteboard. The importance of coding questions in data science interviews cannot be overstated as vast majority of data science roles involves coding on a regular basis.

If we look at the graph above, we can see that there is a wide industry picture when it comes to putting emphasis on coding questions. Airbnb is the absolute champion, with 94% of all questions in our analysis from this company being related to coding. Large tech giants such as Amazon, Apple and Facebook follow suit, although much below Airbnb. Companies such as Walmart (11%) and Goldman Sachs (15%) seem to put less emphasis on coding compared to our average of 34%.

When it comes to questions categorized under coding, the most prominent concept tested was writing SQL queries with emphasis on writing join statements. With SQL being the most utilized tool in data science, it makes perfect sense why these types of questions are asked most often. For example, a question about joins asked on a Facebook interview was: “What is the difference between left join and right join?”

Answer to this question could be something like: “Main difference between left join and right join is in the inclusion of non-matched rows. The LEFT join includes records from the left side and matched rows from the right table while RIGHT JOIN returns all rows from the right side and unmatched rows from the left table.”

Technical ​

Technical questions have been categorized as all questions which are asking about the explanation on various data science technical concepts. Although some of the principals tested are similar to coding questions, technical questions are theoretical and require knowledge on the technology you will be using at the company. For example, technical question would be to explain the process of creating a table in R without using external files. Knowing the theory behind what you are doing is quite important which is why technical questions can be asked on interviews often.

Due to the lower number of technical questions in our research data (48 questions, or around 5%), not all companies from our analysis had questions categorized under technical. We can see that LinkedIn is putting above average emphasis on technical questions with 14% of their questions comprised from this category, compared to the total average of 6%.

In terms of questions categorized as technical, the most tested area is theoretical knowledge on Python and SQL. With these two languages being dominant in the field of data science (along with R to complement Python), it is no surprise that most interviewers want to test theoretical knowledge in these areas. Example of a real-world technical question from Amazon would be: “What is the difference between a list and an array?”

You could answer this question with the following statement: “The main difference between a list and an array is the operation you can perform on them. Lists serve as containers for different data types while arrays store only one data type.”

System Design ​

System design questions are all questions related to designing technology systems. These questions are asked in order to analyze the candidate’s process in solving problems and creating (and designing) systems to help customers/clients. For example, you could be asked to show how you would design a data warehouse for one of the other departments. Knowing system design can be quite important for a data scientist; even if your role is not to design a system, you will most likely play a role in an established system and need to know how it works in order to do your work.

For the same reason as questions categorized as technical (system design comprises 3% of all questions), only a few companies had questions from this area. Walmart is the only organization putting above-average on system design, with 6% of all the interview questions being asked from this category.

Questions categorized under system design have numerous completely different topics and tasks, but when it comes to technical concepts teste, the one that stands out is building a database. Since data scientists deal heavily with databases on an everyday basis, it makes sense to ask this question and verify whether your candidate can build a database from scratch. Here is one question example from Facebook uncovered in our research: “Explain the process of designing a relational database for a ride-sharing app.” Since there is such a variety of approaches to answer this question, we will leave you to come up with your own way of designing one.

Statistics ​

Statistics interview questions have been categorized as all questions which would require knowledge of statistical theory and associated principles. The questions are asked in order to test the interviewee’s knowledge on founding theoretical principles which are used in data science processes. Examples of questions categorized as statistics would be to calculate a sample size or an explanation of the Bayes theorem. These questions are especially significant since being able to understand the theoretical and mathematical background of analyses being done is what every interviewer will appreciate.

Although questions from this category make up about 10% of interview questions on average, we can see there are significant data variations among companies when it comes to this topic. Companies such as Netflix and Lyft are leading the pack here with 33% and 31% of questions being asked from this area respectively. Microsoft (24%) and Twitter (22%) are other companies that have more than double the average of questions from this category on their interviews. It is interesting to note that tech giants and two of the FAANG companies, Amazon (7%) and Facebook (6%) are below average in this category.

When it comes to questions that are under statistics, the most mentioned technical concept is sampling and distribution. This is one of the most basic and most commonly used statistics principles that data scientist can implement on a daily basis. For example, an interview question from IBM asks: “What is an example of a data type with a non-Gaussian distribution?

To answer this question, first we need to know what a Gaussian distribution is. This is a distribution where a certain known percentage of the data can be found when examining standard deviations from the mean, otherwise known as normal distribution. So, to answer this question, you can mention any data type that does not have a normal distribution. Some of the examples can be exponential distribution or binomial distribution.

Probability ​

Probability interview questions are all questions which require theoretical knowledge only on probability concepts. Interviewers ask these questions in order to get a deep understanding of your knowledge on the methods and uses of probability to complete the complex data studies usually performed in the workplace. For example, you could be asked to determine the probability of drawing two cards from the same deck of cards that have the same suite.

Along with system design, probability was the category with the lowest number of questions in our research data, comprising only 3% of all questions. It is therefore no surprise that only 3 companies from our analysis have questions from this area. Goldman Sachs is the only notable outlier here, with 8% of all of their interview questions coming from this category.

Questions related to probability clearly have one technical concept tested the most: probability of getting a certain card/number from a set of dice/cards. This seems to be the most common element of questioning for majority of companies in our research as many of them have asked these types of questions. An example of such probability question, from Facebook: “What is the probability of getting a pair by drawing 2 cards separately in a 52-card deck?

Here is how you can answer this: “This first card you draw can be whatever, so it does not impact the result other than that there is one card less left in the deck. Once the first card is drawn, there are 3 remaining cards in the deck that can be drawn to get a pair. So, the chance of matching your first card with a pair is 3 out of 51 (remaining cards). This means that the probability of this event occurring is 3/51 or 5.89%.”

Product interview questions have been categorized as all questions related to evaluating the performance of a product/service through data. An example of a product question would be to explain the design of an A/B test on the new metric in order to see if it captures meaningful social interactions better. Being able to answer questions about a product is significant as it tests your knowledge on being able to adapt and use data science principles in any environment, as is the case with daily work.

Not all companies from our analysis had product questions as we can see from the diminished graph; however, most of them have even though product questions comprise only 7% of all interview questions on average. Lyft (25%) and Twitter (22%) are the leaders here, with Facebook and Uber following suit (15% each). It is interesting to note that two of these are ride sharing service companies with the remaining two being social media companies. Goldman Sachs is the only notable underperformer in this category, with only 2% of their questions being related to product.

In terms of questions categorized under product, the most prominent technical concept that repeated in questions with multiple companies is to identify a company’s product and propose improvements from a data scientist’s perspective. The high variance in technical concepts tested on the product side can be explained by the nature of product questions and the higher level of creativity that is usually required to answer these. An example of a product improvement question would be: “What is your favourite Facebook product and how would you improve it?” Due to the nature of the question, we will let you answer this one on your own as well.

Business Case ​

Business case questions have been identified as questions involving case studies as well as generic questions related to the business that would test a data science skill . An example of a business case question would be to determine how many windows there are in New York City, or to use the GPS data from a car to determine the quality of the driver. The significance of knowing how to answer these questions can be enormous as some interviewers would like the candidates to know how to apply data science principles to solve company’s specific problems before hiring them.

Since business case category only number about 4% of all questions, it is no surprise that plenty of companies do not have questions from this area. However, Uber is putting an astronomically high value on questions from this category; 25% of all questions on Uber interviews come from this category, more than six times the total average! Twitter is the only other company that has double-digit percentages in this area.

Due to the nature of the question type, we could not really identify a single technical concept which stands out. Since most of the questions categorized here are case studies, each of them is unique in a certain way. However, here is an example of a business case question from Google which is not related to the company, but would test your data science skills: “How many cans of blue paint were sold in the United States last year?”

Answer: “There are 300 Million people in the US. Say there are 100 Million households, in which 1% needs painting, that's 1 Million. Say there are only 1% wants to paint their houses blue, then there are 10,000 houses, which needs 6 cans, then there are 60,000 cans of blue paint for residential painting. Assume there are another 100,000 commercial buildings that paints blue, and each needs 1,000 cans. Thus, the total would be 100 Million + 60,000 cans = 100,060,000 cans.”

Modeling interview questions are categorized as all questions related to machine learning and statistical modelling (regressions). These questions require the knowledge on how to use mathematical models and statistical assumptions to generate sample data and make prediction about real-world events. An example of a modeling questions would be to explain the difference between L1 and L2 regularization for linear regression. For data scientists going into roles with modeling responsibilities, knowing how to answer these questions is crucial as it will most likely be heavily related to their performance.

Modeling was the second largest category in our research data, with 20% of all questions coming from here. There is a lot of variation among companies when it comes to modeling questions. Walmart is the absolute leader in this area, with a staggering 56% of all their questions being categorized under modeling. Other companies above average are machine learning tech giants such as IBM, Microsoft and Netflix. It is interesting to note that Facebook does not put a high emphasis on modeling, with only 3% of their questions from this category.

When it comes to questions categorized under modeling, the most common technical concept asked on interviews is regression. Due to the nature of machine learning and how statistical modeling works, it is no surprise that there are lots of questions on regression. One example from Walmart would be the following: “What is the difference between L1 and L2 regularization for Linear regression?”

Here is how you could answer this question: “A regression model that uses L1 regularization technique is called  Lasso Regression  and model which uses L2 is called  Ridge Regression . The key difference between these two is the penalty term. Ridge regression  adds “ squared magnitude ” of coefficient as penalty term to the loss function whereas Lasso Regression  (Least Absolute Shrinkage and Selection Operator) adds “ absolute value of magnitude ” of coefficient as penalty term to the loss function. The  key difference  between these techniques is that Lasso shrinks the less important feature’s coefficient to zero thus, removing some feature altogether.”

Algorithms ​

Questions on algorithms are categorized as all questions which require solving a mathematical problem, mostly through code by using one of the programming languages. These questions involve a step-by-step process usually requiring adjustment or computation to produce an answer. An example of an algorithmic question would be to find a square root of a number using Python. These questions are important to test the basic knowledge of problem-solving and data manipulation which can be implemented for complex problems at work.

Questions on algorithms on average comprised 14% of all questions we have collected. When we look at companies which have question from this area, we can see that Goldman Sachs is an absolute leader with 63% of their questions being under algorithms. Other notable companies are LinkedIn and Spotify, and those are the only three companies that are above 20%. All other organizations had scores around the mean, with ride sharing services Lyft (6%) and Uber (5%) being the poorest performers.

The technical concept tested most on questions categorized under algorithms is solving a mathematical or syntax problem with a programming language. Since the concepts tested under algorithms are intended to demonstrate problem solving of such nature, it makes sense as to why this is the most common topic. Here is an example: “How would you count the number of occurrences of a letter in a word using Python? “

Here’s the approach you should have to be able to answer them. First of all, to be able to search a word in a statement or a letter in a word, you need to look over the string. Let’s say you have the following example:

statement= “ I love StrataScratch, it helped me get much better at SQL”

to find the number of occurrences of the letter ‘t’, you need first to loop over the statement. Then compare each element of the statement to the letter ‘t’, if the element is truly a ‘t’ then you count one occurrence!

Here’s the pseudo-code:

This data science interview guide has been written in order to support the research undertaken to understand the types of questions being asked at a data science interview. We have taken the interview questions’ data from dozens of companies over a four-year period and compiled it for analysis. As part of the research process, the questions have been categorized under nine different question types (algorithms, business case, coding, modeling, probability, product, statistics, system design and technical questions).

Our analysis of data has resulted in some interesting findings. We saw that Facebook is a dominant company when it comes to data science interview questions, followed by Amazon. Furthermore, we found out which companies give the most emphasis on coding and algorithm question types, as well as which companies ask the most questions in other analyzed categories. Finally, we looked at the breakdown of FAANG companies and got some interesting insights there.

As part of our analysis, we talked about some of the most common technical concepts from each of the question type categories. For example, we discovered that the most asked statistics’ questions have to do with sampling and distribution.

The article is intended to serve you as an important guide; whether you just want to learn more about data science, want to brush up on your skills or in the interview preparation process. We hope you have gained plenty of valuable insights from our research and now feel more comfortable about the data science interview process.

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Top Case Studies in Data Science Interview

Let’s explore some top case studies in data science interviews

Data science interviews are becoming increasingly common as organizations seek to leverage data-driven insights for strategic decision-making. These interviews often include case studies that assess a candidate’s ability to apply data science techniques to real-world problems. Let’s explore some top case studies in data science interviews.

Customer Segmentation: Companies often want to understand their customer base better to tailor their marketing strategies. In this case study, candidates may be asked to segment customers based on various factors such as demographics, purchasing behavior, or geographic location.

Churn Prediction: Predicting customer churn is critical for businesses looking to retain customers and maximize revenue. Candidates may be presented with data on customer interactions and asked to build a predictive model to identify customers at risk of churning.

Recommendation Systems: Recommendation systems are widely used in e-commerce , streaming services, and social media platforms to personalize user experiences. Candidates may be tasked with designing a recommendation algorithm based on user preferences and historical data.

Predictive Maintenance: Predictive maintenance helps companies anticipate equipment failures and minimize downtime. Candidates may be given sensor data from machinery and asked to develop a model that predicts when maintenance is required.

Fraud Detection: Fraudulent practices can lead to considerable financial losses for businesses. Candidates may be provided with transactional data and tasked with building a fraud detection model that can identify suspicious patterns and anomalies.

Sentiment Analysis: Sentiment analysis involves analyzing text data to determine the sentiment or opinion expressed within it. Candidates may be asked to analyze customer reviews or social media posts to gauge public sentiment towards a product or brand.

Time Series Forecasting: Time series forecasting involves predicting future values based on past observations. Candidates may be given historical data, such as stock prices or sales figures, and asked to develop a forecasting model to predict future trends.

Image Classification: Image classification involves categorizing images into predefined classes or categories. Candidates may be provided with a dataset of images and asked to build a classification model that can accurately identify objects or patterns within the images.

Natural Language Processing (NLP): NLP techniques are used to extract insights from unstructured text data. Candidates may be tasked with building a text classification model, performing named entity recognition, or generating text summaries.

A/B Testing: A/B testing is a method used to compare two versions of a product or service to determine which performs better. Candidates may be presented with A/B testing results and asked to interpret the findings and make recommendations based on the data.

In conclusion, case studies in data science interviews offer candidates the opportunity to showcase their problem-solving skills, analytical abilities, and domain knowledge. By preparing for these case studies and understanding the underlying concepts, candidates can increase their chances of success in data science interviews and contribute effectively to organizations leveraging data science for decision-making.

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• Published: 02 April 2024

Cost of start-up activities to implement a community-level opioid overdose reduction intervention in the HEALing Communities Study

• Iván D. Montoya 1 ,
• Colleen Watson 2 ,
• Arnie Aldridge 2 ,
• Danielle Ryan 3 ,
• Sean M. Murphy 3 ,
• Brenda Amuchi 4 ,
• Kathryn E. McCollister 1 ,
• Bruce R. Schackman 3 ,
• Joshua L. Bush 5 ,
• Drew Speer 5 ,
• Kristin Harlow 6 ,
• Stephen Orme 2 ,
• Gary A. Zarkin 2 ,
• Mathieu Castry 4 ,
• Eric E. Seiber 6 ,
• Joshua A. Barocas 7 ,
• Benjamin P. Linas 4 &
• Laura E. Starbird   ORCID: orcid.org/0000-0003-4056-2422 8  

Addiction Science & Clinical Practice volume  19 , Article number:  23 ( 2024 ) Cite this article

115 Accesses

Metrics details

Communities That HEAL (CTH) is a novel, data-driven community-engaged intervention designed to reduce opioid overdose deaths by increasing community engagement, adoption of an integrated set of evidence-based practices, and delivering a communications campaign across healthcare, behavioral-health, criminal-legal, and other community-based settings. The implementation of such a complex initiative requires up-front investments of time and other expenditures (i.e., start-up costs). Despite the importance of these start-up costs in investment decisions to stakeholders, they are typically excluded from cost-effectiveness analyses. The objective of this study is to report a detailed analysis of CTH start-up costs pre-intervention implementation and to describe the relevance of these data for stakeholders to determine implementation feasibility.

This study is guided by the community perspective, reflecting the investments that a real-world community would need to incur to implement the CTH intervention. We adopted an activity-based costing approach, in which resources related to hiring, training, purchasing, and community dashboard creation were identified through macro- and micro-costing techniques from 34 communities with high rates of fatal opioid overdoses, across four states—Kentucky, Massachusetts, New York, and Ohio. Resources were identified and assigned a unit cost using administrative and semi-structured-interview data. All cost estimates were reported in 2019 dollars.

State-level average and median start-up cost (representing 8–10 communities per state) were $268,657 and $175,683, respectively. Hiring and training represented 40%, equipment and infrastructure costs represented 24%, and dashboard creation represented 36% of the total average start-up cost. Comparatively, hiring and training represented 49%, purchasing costs represented 18%, and dashboard creation represented 34% of the total median start-up cost.

We identified three distinct CTH hiring models that affected start-up costs: hospital-academic (Massachusetts), university-academic (Kentucky and Ohio), and community-leveraged (New York). Hiring, training, and purchasing start-up costs were lowest in New York due to existing local infrastructure. Community-based implementation similar to the New York model may have lower start-up costs due to leveraging of existing infrastructure, relationships, and support from local health departments.

The opioid overdose crisis is one of the most pressing public health issues in the United States. Community-based opioid overdose education and naloxone distribution (OEND) programs, medications for opioid use disorder (MOUD), and safer prescribing and dispensing education are effective public health interventions to prevent opioid-related overdose [ 11 , 14 , 18 ]. Despite the demonstrated efficacy of these evidence-based practices (EBPs) to support harm reduction, treatment, and recovery from opioid use disorder (OUD), there are substantial barriers to implementing them. Fewer than 20% of people with OUD receive recommended treatment and services [ 19 ]. Reasons for underutilization of these services include a limited number of MOUD prescribing providers, lack of screening for OUD by healthcare and justice systems, lack of treatment capacity for MOUD, lack of access and awareness among individuals with OUD about treatment options, and stigma surrounding the use of MOUD [ 3 , 9 , 10 , 19 ].

Communities That Heal (CTH) is a community-engaged intervention designed to reduce opioid overdose fatalities by increasing the adoption and delivery of EBPs and reducing stigma in healthcare, behavioral health, criminal justice, and other key settings [9]. The CTH intervention relies on community engagement to assist key stakeholders in using data-driven techniques to select and implement EBPs, and a communication campaign to educate the community, address stigma, and build demand for EBPs. In the CTH model, communities establish a coalition of stakeholders from sectors including medical and mental health services, substance use treatment and harm reduction services, law enforcement and corrections, education, social services, local government, and individuals with lived experience. Guided by their community’s opioid-related data (e.g., overdose trends, hotspot mapping, law enforcement activity), each coalition selects strategies from a menu of EBPs targeting OEND, MOUD, and safer prescribing, and guides the implementation of these strategies. In addition, community coalitions deploy a series of communications campaigns to reduce stigma and increase uptake of MOUD and OEND. The HEALing Communities Study (HCS) is a multi-site, parallel group, cluster randomized, wait-list controlled trial to implement and evaluate the effect of the CTH intervention on reducing opioid overdose deaths in disproportionately affected communities located in four states– Kentucky, Massachusetts, New York, and Ohio. The goal of the HCS is to produce generalizable information for policy makers and community stakeholders seeking to implement CTH or similar community-driven interventions [ 1 ].

Implementing CTH requires communities to invest substantial time and resources in establishing a community-driven process for EBP selection and implementation. In a large-scale community intervention framework such as CTH, assessing these initial investments is critical and more complex than in a traditional individual-level randomized trial. This paper therefore describes the economic costs of the start-up phase of CTH that encompasses the activities required to begin implementing the intervention in the 34 communities randomized to initiate CTH in the first wave of the wait-list controlled HCS study. While recent economic evaluations have demonstrated the value of pharmacologic interventions for OUD [4–6, 17], start-up costs are frequently excluded from these economic evaluations. Few studies have reported start-up costs related to OEND and MOUD implementation [ 2 , 7 ] and to our knowledge, this is the first study to report the initial investment required to implement a complex, large-scale, community-driven approach to addressing the opioid crisis in the United States.

We defined, measured, and valued the costs of start-up investments in four HCS study sites located in Kentucky, Massachusetts, New York, and Ohio. A total of 34 rural and urban intervention communities (counties, townships, or metropolitan areas) across these four sites, 8 communities in 3 states and 10 in Ohio, were randomized to implement the CTH intervention. We identified three different models of operating across the four sites: hospital-academic (Massachusetts), university-academic (Kentucky and Ohio), and community-leveraged (New York). The hospital-academic and university-academic models primarily hired staff through one institution while also taking advantage of the expertise of existing academic faculty. The community-leveraged model primarily hired or used existing staff at local government or community-based organizations, while also taking advantage of the expertise of faculty and staff located at an academic institution.

We estimated costs from the community perspective, reflecting the investments that a community would need to incur during preparation to implement the CTH intervention. In this case, community may be defined as a local or county government, health department, health system, or community-based organization; 24 of the communities in the HCS study represented counties and the other 10 represented units smaller than counties.

We adopted an activity-based costing approach in which the activities and resources to implement the start-up phase were first identified and then assigned unit costs. We defined start-up costs as all one-time, preparatory expenses incurred from inception of the CTH design until the CTH community coalitions were formed and functioning. We included all relevant costs that were incurred during the HCS trial preparation phase from May 2019 through December 2019, as well as costs incurred for start-up activities that occurred during the early intervention phase through April 2020. All costs are reported in 2019 U.S. dollars. Costs associated with implementing the CTH intervention will be presented in future analyses. HCS research costs (e.g., data collection and IRB compliance training, and staff hired to support research operations) during these time periods were excluded, which is consistent with our goal of understanding the resources needed to reproduce the intervention start-up outside of a research environment.

We identified four start-up cost categories: hiring intervention staff; training intervention staff; equipment and infrastructure; and costs to develop community online dashboards. We used a standardized instrument to systematically collect data across the four HCS sites for each of these categories.

All methods were carried out in accordance with the protocol and guidelines established by the Healing Communities Steering Committee and by Advarra Inc., the HEALing Communities Study single Institutional Review Board (IRB). Furthermore, the start-up cost analysis plan and methodology were developed by the Health Economics Workgroup (HEWG), which includes health economist members from the 4 study sites. Cost data collection was carried out through a standardized process agreed upon by all HEWG members. Policies and procedures to conduct semi-structured interviews with administrative staff were approved by the study IRB. In order to participate in the semi-structured interviews, staff received a verbal informed consent description and had to agree before the interview could commence. Staff informed consent was documented in REDCap. All subjects agreed to participate and provided consent.

Hiring costs

Hiring costs include time invested by individuals involved in hiring intervention staff including human resources personnel, legal personnel, project directors, faculty (excluding time spent on research exclusive hiring activities), and community-level staff. The process of hiring staff varied across the sites. In Kentucky and Ohio, CTH intervention staff were hired as university employees. We conducted semi-structured interviews with the administrative staff who performed hiring activities at each university to understand the time they spent hiring CTH intervention staff beginning in May 2019 through December 2019. Informed consent was obtained from administrative staff and other respondents before collecting interview data. Interviewers walked interviewees through a standardized form and asked questions that would allow completion of the form (included in Additional file 1 : Material). In Massachusetts, the intervention staff members were hired as hospital and university employees between July 2019 and December 2019, and hiring data were collected in interviews with relevant staff. In New York, intervention staff were hired by each CTH community’s local health department or lead community-based organization between October 2019 and April 2020, and time estimates for the hiring process were obtained individually from administrative staff in each of the communities. We recorded time spent by each hiring staff member on pre-hire activities, such as creating and posting job descriptions, reviewing applicants, interviewing applicants, hiring decision-making, and general onboarding activities (not including CTH-specific trainings, which were captured separately as training costs). An average per-hire time commitment was then calculated and applied to every intervention staff member hired.

The labor costs associated with these time estimates were calculated using salaries and fringe benefits that were obtained from site invoices, self-report and publicly available data sources. In a real-world scenario, CTH staff would likely not be hired by academic centers. Therefore, to account for the likelihood that community-level staff would be responsible for hiring, we replaced the actual academic researcher wages with wages based on comparable community occupations (i.e., Medical and Health Service Managers, Payroll and Timekeeping Clerks, across all sites and applied a nationally representative 34% fringe rate [ 16 ].

Training costs

In Kentucky and Ohio, faculty and staff coordinated a central training for new intervention staff at the beginning of the intervention implementation period (i.e., post-December 2019). The original training was conducted in-person, but it was also recorded for viewing by subsequent hires. Training sessions in Massachusetts were conducted both in-person and virtually and were recorded and saved for future hires to view. In New York, both live and virtual training sessions were conducted, and each new hire completed a pre-recorded series of training modules.

The cost of training staff was valued using two time components: (1) time spent by individuals performing the training, and (2) time spent by intervention staff being trained. Time spent by trainers preparing materials and pre-recording trainings was estimated in interviews. Time spent by trainers and trainees in live and virtual sessions was estimated using study records detailing the training sessions at each site. Similar to calculating hiring costs, the labor costs associated with these time estimates were calculated using salaries and fringe benefits that were obtained from site invoices and human resources reports. To account for real-world implementation where academic faculty likely would not lead the intervention, we replaced the actual academic researcher wages with wages based on comparable community-based roles (i.e., Medical and Health Service Managers, Mental Health and Substance Abuse Social Workers, and Health Education Specialists) across all sites and applied a 34% fringe rate. In the few instances of staff turnover, start-up hiring cost estimates were limited to one instance per position filled.

Equipment and infrastructure costs

The costs of physical space, information technology (IT) services, and equipment needed to prepare for CTH implementation were gathered from invoices, purchasing records, and interviews. The Kentucky site hired staff who lived in or near each intervention community and placed them in space at local universities, public health departments, or other suitable office locations. We recorded start-up costs associated with obtaining this space, IT infrastructure (e.g., access to wireless internet on the University of Kentucky network), and teleconferencing equipment for virtual interaction. The Ohio and Massachusetts sites reported equipment purchases, such as phones and laptops for intervention staff, but no new space or IT infrastructure costs. The New York site reported software costs and equipment costs such as phones, laptops, and desks. The New York site also did not have added space or IT infrastructure costs because the intervention staff were housed primarily in existing county health department and local partner organization offices.

Community dashboard portal costs

The CTH intervention includes a community dashboard portal for sites to view and share community-specific data with their community stakeholders, and for stakeholders to use to inform community-level decisions about which EBPs to implement. The cost of establishing the portals was derived from time spent by CTH staff to help design the portal, time spent by computer programmers to create the portal, and in some cases, the cost of new software to create and/or host the portal. Costs associated with the time estimates were calculated using salaries and fringe benefits that were obtained from site human resource records and self-report.

We entered, cleaned, and analyzed data using a standardized MS-Excel spreadsheet. This data collection tool was standardized but flexible enough for sites to tailor data collection to their specific needs and included separate worksheets for each of the start-up cost categories (hiring, training, infrastructure and equipment, and dashboard costs). All sites had salary and fringe benefit information available for current staff and staff who were hired for the CTH intervention. In addition to using administrative data on hiring and training costs from each site, we also obtained standardized wage data from the Occupational Information Network (O*NET) website and applied a 34% fringe rate in order to compare the local site costs to regional and national costs for similar positions [15]. O*NET is a comprehensive database containing occupational characteristics, wages, and other information obtained through national surveys of sampled workers, occupation experts, and occupation analysts. O*NET wages were assigned by matching the average state and national salaries of occupational titles with similar job titles and duties as the CTH intervention staff (Additional file 1 : Table S2).

We calculated start-up cost per capita for each state using the total population of Wave 1 HEALing Communities Study communities. Total community population represents the societal pool that would be responsible for the cost of the CTH intervention. The 24 county population estimates were retrieved from the 2020 Bridged-Race Population Estimates [ 12 ] and for the 10 communities that represent units smaller than counties, we used population estimates from the 2017–2021 American Community Survey 5-Year Averages [ 17 ].

Table 1 summarizes the cost of each of the sites’ start-up cost to prepare to implement the CTH intervention by each of the four cost categories (hiring, training, infrastructure and equipment, and dashboard costs). Across the four sites, the total state-level mean and median start-up costs were $247,673 and $175,683, respectively (range $149,776 to $358,404). The population was 786,387 in Kentucky, 451,629 in Massachusetts, 1,382,518 in New York, and 3,006,020 in Ohio. The resulting start-up cost per capita was $0.46 in Kentucky, $0.67 in Massachusetts, $0.11 in New York, and $0.06 in Ohio. Hiring and training represent 40% of the average total start-up cost. Infrastructure and equipment represent 24% of the average total start-up cost but 18% of the median cost; this difference is attributable to higher costs in Kentucky, due to investments in infrastructure for new space in participating communities. Dashboard costs represented 36% of average total startup costs but varied widely among the sites, from 6% of start-up costs in Kentucky to 71% of start-up costs in Massachusetts.

Across all four sites, a total of 45 staff were involved in hiring 71 intervention staff needed to begin implementing the CTH intervention. The mean and median time spent by hiring staff were 771 h and 438 h, respectively. The mean and median cost of hiring across the four intervention sites was $45,018 and $28,992, respectively (Table  2 ). In total for all four sites, hiring costs were $180,072. The Kentucky site accounted for 63% ($112,602) of this cost, despite using fewer hiring staff (n = 6) than the New York site (n = 25), which accounted for 5% ($9,487) of total hiring costs (Fig.  1 ). These differences may be attributed to the number of new staff hired. There were 41 staff who were not “new” hires but whose time was reallocated into CTH intervention activities, and therefore did not have hiring costs, but who were trained on the CTH intervention resulting in a total of 112 staff who received training from 84 trainers.

Site contribution of total start-up costs by category

Table 1 also reports total training cost by site. CTH intervention staff were trained on community engagement, communications campaign, and the three pillars of the EBP menu: MOUD, OEND, and safer prescribing [9]. Eleven individuals spent time as both trainers and trainees; their time was applied to the total number of hours in both groups, but their trainer and trainee hours were allocated separately (i.e., not duplicated). Trainers spent an average 230 h and median 125 h training the 71 newly hired, and 41 intervention staff whose time was reallocated (Table  2 ). The average and median cost of trainer time across sites was $15,681 and $10,714 respectively. Most of the trainer time consisted of developing and preparing materials to conduct the training rather than time with the trainees themselves.

The intervention staff across all four sites accumulated an average of 913 h (median of 798 h) being trained in the CTH intervention. The average and median cost associated with staff who received training across the four sites is $38,610 and $35,631 respectively. Massachusetts had the lowest training cost across the four sites, accounting for 19% of the overall training costs for all four sites, compared to the New York site which had the highest training cost, accounting for 30% of the total training costs (Fig.  1 ).

Table 2 reports average and median labor costs for hiring and training across the four sites using reported wages and O*NET wages at the state and national level. The mean and median cost of labor to prepare for the implementation of the CTH intervention across all four sites using administrative data are $99,310 and $75,326 respectively. Mean costs were 8–12% higher and median costs were 30–35% higher using state or national wage data. The impact of the different wages varied by site, although Ohio was the only site where using standardized O*NET wages resulted in lower total labor costs than using reported salaries and fringe benefits (Additional file 1 : Table S3).

Additional file 1 : Table S4 summarizes equipment, infrastructure, and dashboard costs by site. The Kentucky site accounted for approximately half of the total equipment cost across the sites, with a cost of $79,552. In Kentucky, equipment costs were identified through invoices and study records and included reimbursed mileage and travel time to and from communities in which the CTH intervention would be implemented, which are captured as “Other” costs. The Kentucky site’s equipment cost also included IT specialist time needed to mount and install equipment in community office space. New York and Massachusetts had the lowest equipment costs of $14,845 and $17,125 respectively, likely because CTH intervention staff were housed in offices that already had some equipment in place for their own staff.

The Kentucky site was alone in incurring new infrastructure costs to prepare for CTH implementation totaling $77,305. These space and infrastructure costs include time spent by administrative staff to select space and costs of executing contracts for new leases. The Kentucky site also paid quarterly leases for the space needed to support the CTH staff in multiple communities. During the start-up period, lease payments totaled $134,180 but are not included in infrastructure estimates to remain consistent with the other sites.

The cost to construct community dashboard portals varied by site due to differences in processes for the portal and dashboard creation. The cost of Kentucky’s dashboard was the lowest at $21,654, which included the time of a computer programmer using open-source software and libraries (free to anyone on the internet). Massachusetts incurred the highest cost at $212,408, which consisted of informatics, data management and programming, website development and hosting, and non-service (hardware and software) costs. The New York and Ohio sites reported similar costs of $60,006 and $57,752, respectively. The New York site contracted out to a university information technology department for development and software. The Ohio site used university personnel proficient in software development to develop the portal, data science engineers to develop dashboards, and system engineers to articulate the infrastructure for hosting.

This cost analysis presents the costs incurred to prepare for the implementation of the CTH intervention in 34 unique communities throughout Kentucky, Massachusetts, New York, and Ohio. These are the first reported initial investments required to implement a large-scale, community-driven approach to address the opioid epidemic. The implementation of evidence-based practices should account for the economic implications of starting a new intervention or approach, which are rarely reported in the economic evaluation literature. Reporting on the investments can inform future communities and policymakers of the resources, time, and costs required to initiate a community-level intervention at this scale. The start-up costs from this analysis may be generalizable to any community-level intervention that follows a coalition-based, data-driven model, as the processes for hiring, training, and infrastructure in CTH likely would be similar regardless of health outcomes targeted. Due to key site-level differences in implementation approach, our analysis also allows stakeholders to view implementation through three distinct staffing models: hospital-academic (Massachusetts), university-academic (Kentucky and Ohio), and community-leveraged (New York).

Both labor and non-labor costs varied widely across research sites. In general, hiring and training staff for the implementation of the CTH intervention were the largest cost components consistently across all sites due to the time commitment required for hiring, gathering and preparation of training materials, conducting training, and the time commitment of the new hires being trained. While the elements of hiring and training labor costs were similar across sites, labor cost estimates varied depending on the breakdown of staff types involved in training and hiring and how the sites staffed the CTH intervention. In Massachusetts, substantially more staff were involved in hiring compared to Kentucky, for example, yet overall hours spent hiring new staff in Massachusetts was substantially less than in Kentucky. This could be explained by the HEAL-specific career fairs hosted by the Massachusetts site, which led to invitations for group interviews/simulations with groups of staff. Additionally, due to their hospital-academic model of implementation, the Massachusetts site was able to reallocate existing staff time in many cases to create a CTH intervention team rather than bring on new hires. On the other hand, the New York site leveraged community resources to lead hiring efforts; health departments and local organizations in each community implementing the CTH intervention led the hiring process independently which led to variability in hiring costs across the individual communities. Overall, 25 different staff in New York were involved in hiring, many more than Kentucky and Massachusetts, but New York communities reported spending substantially less total time hiring than Kentucky or Massachusetts. One possible explanation is that in New York each community hired local individuals who were already known to the health departments due to their work history in the substance use field. Additionally, as New York community sites were not affiliated with a university or a hospital, they may have had a less time-consuming and bureaucratic process for hiring staff. On the other hand, the New York community model incurred higher training costs, which may be a consequence of bringing on non-academic partners to implement an intervention. The hiring and training costs incurred by the New York site may represent more real-world costs to a community replicating the CTH intervention compared to the other sites.

Equipment, infrastructure, and dashboard costs varied widely across the four CTH sites. Kentucky had the highest equipment cost and was the only site reporting new infrastructure purchases and space leasing costs. These costs were incurred for the purpose of intervention staff having their own space in their respective communities, and to reduce staff travel time to complete intervention activities considering the long distance between communities and the University of Kentucky. The extent to which future communities would need to invest in these types of designated spaces may vary; however, Kentucky’s model provides valuable insight for communities with similar space needs. In this case, infrastructure investments were integral to supporting the launch and ongoing activities of CTH. While the other sites did not incur new infrastructure costs, it is important to note the opportunity cost of existing infrastructure and that leveraging existing space means that space is no longer available for other programs. Therefore, stakeholders should consider full organizational needs when determining the cost of new versus existing infrastructure. In Massachusetts, CTH intervention staff had office space within the centralized hospital-academic system from which they traveled to their respective communities. However, the analysis presented in this manuscript only includes travel related to infrastructure preparation and/or training. Future cost analyses of CTH will include travel costs related to implementing the intervention. In comparison to Kentucky, travel times to communities in Massachusetts were shorter. Kentucky, New York, and Ohio had relatively low dashboard and portal costs compared to Massachusetts. This was due to the availability of software developers at academic institutions who were involved in creating the dashboards. Additionally, open-source frameworks, libraries, and software were utilized in the creation of these dashboards, which may contribute to reducing the cost of dashboard creation. In comparison, Massachusetts’ high dashboard costs occurred due to outsourcing the creation of the dashboard and website hosting.

Despite attempts to standardize collection of start-up costs, our findings are limited by the quality and heterogeneity of our data. The start-up of the CTH intervention was not completely standardized across the sites, and therefore processes to prepare for implementation differed across each site depending on whether the site followed a university-academic, hospital-academic, or community-leveraged model. This ranged from how employees were hired and trained, the start-up phase timeline, and some cost data that were not collected consistently by all sites (e.g., travel costs related to infrastructure and space preparation). Interpretation of cost per capita presented in this analysis is limited in that it does not account for OUD prevalence, which would be a more direct measure of cost per target population. Although the target population of CTH is those with OUD, the per capita cost represents the cost burden of those who will pay the start-up costs (i.e., the broader community). Decisionmakers may choose to weigh per capita costs by OUD prevalence to inform spending of public health resources based on the target population. Furthermore, the COVID-19 pandemic interrupted the end of the start-up phase, and it is unclear how the number of staff hired or the training modalities may have been impacted by the pandemic. To capture all relevant start-up costs we extended the start-up data collection phase 4 months into the implementation of the intervention, but we do not include the cost of developing or deploying the CTH intervention in our start-up analysis. The ability to report on four different research sites’ processes for preparing to implement this opioid overdose intervention is a unique contribution to the field and can help stakeholders understand the potential resources involved in a wide-scale community-engaged intervention such as the CTH intervention.

The variation in start-up cost may be of interest to policymakers when deciding how to initiate implementation of the CTH intervention and other large-scale community-based interventions. Implementation using a community-leveraged model similar to the one used by the New York site may be appealing. Hiring and training labor costs and other costs were the lowest for this site due to existing infrastructure, relationships, and support from local organizations. Kentucky and Ohio followed a more centralized top-down approach to the start-up period, specifically for hiring, which may require that the CTH intervention be driven by an academic institution. The Kentucky site also provides an example of the cost to implement an intervention in communities with little existing infrastructure and may inform start-up in rural communities. We found that in communities where there is existing infrastructure, start-up may be less costly if resources are able to be allocated to the new intervention without burdening other programs. Overall, the modest cost burden of $0.06 to $0.67 per community member demonstrates the feasibility of all four start-up models for a large-scale community-level intervention.

Availability of data and materials

All data generated or analyzed during this study are included in this published article. Administrative wage data generated and analyzed during this study are not publicly available due to their sensitive nature. However, generalizable wage data can be found on O*NET ( www.onetonline.org ). Dr. Kathryn E. McCollister ( [email protected] ) may be contacted for further wage data requests.

Abbreviations

Medications for opioid use disorder

Evidence based practice

• Opioid use disorder

Communities that HEAL

HEALing Communities Study

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Acknowledgements

Authors would like to thank Karrie Adkins, a Community Coordinator working at the University of Kentucky for providing invaluable on the ground contextual information about the start-up phase. We wish to acknowledge the participation of the HEALing Communities Study communities, community coalitions, and Community Advisory Boards and state government officials who partnered with us on this study. We would also like to thank the HCS Health Economics Work Group (HEWG) and the Implementation Science Work Group (ISWG) for their contribution to and review of this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Substance Abuse and Mental Health Services Administration or the NIH HEAL Initiative.

This research was supported by the National Institutes of Health and the Substance Abuse and Mental Health Services Administration through the NIH HEAL Initiative under award numbers UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, UM1DA049417 (ClinicalTrials.gov Identifier: NCT04111939).

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Colleen Watson, Arnie Aldridge, Stephen Orme & Gary A. Zarkin

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Danielle Ryan, Sean M. Murphy & Bruce R. Schackman

Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA

Brenda Amuchi, Mathieu Castry & Benjamin P. Linas

College of Public Health, University of Kentucky, Lexington, KY, USA

Joshua L. Bush & Drew Speer

College of Public Health, The Ohio State University, Columbus, OH, USA

Kristin Harlow & Eric E. Seiber

Sections of General Internal Medicine and Infectious Diseases, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Joshua A. Barocas

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IDM, AA, SMM, KEM, BRS, KH, SO, GAZ, JAB, BPL and LES: Conceived and designed the methodology. CW, DR, DS, JLB, BA: Collected data and contributed to data harmonization. IDM and CW: Performed analysis of data. IDM and LES: Wrote and edited manuscript. SMM, KEM, BRS, DS, MC, EES, BPL: Reviewed and edited manuscript.

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Montoya, I.D., Watson, C., Aldridge, A. et al. Cost of start-up activities to implement a community-level opioid overdose reduction intervention in the HEALing Communities Study. Addict Sci Clin Pract 19 , 23 (2024). https://doi.org/10.1186/s13722-024-00454-w

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Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China

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• Published: 06 April 2024

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• Xiaokang Zuo 1 &
• Hanxi Wang   ORCID: orcid.org/0000-0003-4130-6981 1 , 2  

The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

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Acknowledgements

The authors thank the reviewers for their valuable comments, and the authors thank the editor for his efforts in this paper.

This research was funded by the High-level Talent Foundation Project of Harbin Normal University (No. 1305123005).

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Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions/School of Geographical Sciences, Harbin Normal University, Harbin, 150025, China

Xiaokang Zuo & Hanxi Wang

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Xiaokang Zuo: Ms. Huang drafted the initial manuscript, analyzed data, reviewed the manuscript, and approved the final manuscript as submitted. Hanxi Wang: Mr. Wang conceptualized and designed the study, reviewed and revised the manuscript, and approved the final manuscript as submitted. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.

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Zuo, X., Wang, H. Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33149-5

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