case study for data analyst interview

Data Analytics Case Study: Complete Guide in 2024

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

• What metrics should I propose?
• How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

TRY CHECKING: Marketing Analytics Case Study Guide

Data Analytics Case Study Question: Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

• If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
• If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

• Who uses this social network? How do they interact with each other?
• Has there been any performance issues that might be causing the problem?
• What are the goals of this particular launch?
• Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

• The decreasing comments issue isn’t a result of a declining user base.
• The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

• January: 10000 users, 30000 comments, 3 comments/user
• February: 20000 users, 50000 comments, 2.5 comments/user
• March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

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The Data Monk

Case study interview questions for analytics – day 5, top categories.

Topic – Case Study Interview Questions How to solve case study in analytics interview? Solving a case study in an analytics interview requires a structured and analytical approach. Here are the steps you can follow to effectively solve a case study:

• Understand the Problem : Begin by carefully reading and understanding the case study prompt or problem statement. Pay attention to all the details provided, including any data sets, context, and specific questions to be answered.
• Clarify Questions : If anything is unclear or ambiguous, don’t hesitate to ask for clarification from the interviewer. It’s crucial to have a clear understanding of the problem before proceeding.
• Define Objectives : Clearly define the objectives of the case study. What is the problem you are trying to solve? What are the key questions you need to answer? Having a clear sense of purpose will guide your analysis.
• Gather Data : If the case study provides data, gather and organize it. This may involve cleaning and preprocessing the data, handling missing values, and converting it into a suitable format for analysis.
• Explore Data : Conduct exploratory data analysis (EDA) to gain insights into the data. This includes generating summary statistics, creating visualizations, and identifying patterns or trends. EDA helps you become familiar with the data and can suggest potential directions for analysis.
• Hypothesize and Plan : Based on your understanding of the problem and the data, formulate hypotheses or initial ideas about what might be driving the issues or opportunities in the case study. Develop a plan for your analysis, outlining the steps you will take to test your hypotheses.
• Conduct Analysis : Execute your analysis plan, which may involve statistical tests, machine learning algorithms, regression analysis, or any other relevant techniques. Ensure that your analysis aligns with the objectives of the case study.
• Interpret Results : Once you have conducted the analysis, interpret the results. Are your findings statistically significant? Do they answer the key questions posed in the case study? Use visualizations and clear explanations to support your conclusions.
• Make Recommendations : Based on your analysis and interpretation, provide actionable recommendations or solutions to the problem. Explain the rationale behind your recommendations and consider any potential implications.
• Communicate Effectively : Present your findings and recommendations in a clear and structured manner. Be prepared to explain your thought process and defend your conclusions during the interview. Effective communication is essential in analytics interviews.
• Consider Business Impact : Discuss the potential impact of your recommendations on the business. Think about how your solutions might be implemented and the expected outcomes.
• Ask Questions : At the end of your analysis, you may have an opportunity to ask questions or seek feedback from the interviewer. This shows your engagement and curiosity about the case study.
• Practice, Practice, Practice : Preparing for case studies in advance is crucial. Practice solving similar case studies on your own or with peers to build your problem-solving skills and analytical thinking.

Remember that in analytics interviews, interviewers are not only assessing your technical skills but also your ability to think critically, communicate effectively, and derive meaningful insights from data. Practice and a structured approach will help you excel in these interviews Case Study Interview Questions

Customer Segmentation Case Study

Customer Segmentation: You work for an e-commerce company. How would you use data analytics to segment your customers for targeted marketing campaigns? What variables or features would you consider, and what techniques would you apply to perform this segmentation effectively?

Segmenting customers for targeted marketing campaigns is a crucial task for any e-commerce company. Data analytics plays a pivotal role in this process. Here’s a step-by-step guide on how you can use data analytics to segment your customers effectively:

• Demographic information (age, gender, location)
• Purchase history (frequency, recency, monetary value)
• Website behavior (pages visited, time spent, products viewed)
• Interaction with marketing campaigns (click-through rates, open rates)
• Customer feedback and reviews
• Data Cleaning and Preprocessing : Clean and preprocess the data to ensure accuracy and consistency. Handle missing values, outliers, and inconsistencies in the data. Convert categorical variables into numerical representations using techniques like one-hot encoding or label encoding.
• Feature Engineering : Create new features or variables that could be valuable for segmentation. For example, you might calculate the average order value, customer lifetime value, or purchase frequency.
• RFM (Recency, Frequency, Monetary) scores for purchase behavior
• Demographic variables such as age, gender, and location
• Customer engagement metrics like click-through rates or time spent on the website
• Product category preferences
• K-Means Clustering : Groups customers into clusters based on similarities in selected variables.
• Hierarchical Clustering : Divides customers into a tree-like structure of clusters.
• DBSCAN : Identifies clusters of arbitrary shapes and densities.
• PCA (Principal Component Analysis) : Reduces dimensionality while preserving key information.
• Machine Learning Models : Utilize supervised or unsupervised machine learning algorithms to find patterns in the data.
• Segmentation and Interpretation : Apply the chosen segmentation technique to the data and segment your customer base. Interpret the results to understand the characteristics of each segment. Assign meaningful labels or names to the segments, such as “High-Value Shoppers” or “Casual Shoppers.”
• Validation and Testing : Evaluate the effectiveness of your segmentation by assessing how well it aligns with your business goals. Use metrics such as within-cluster variance, silhouette score, or business KPIs like revenue growth within each segment.
• Targeted Marketing Campaigns : Design marketing campaigns tailored to each customer segment. This could involve personalized product recommendations, email content, advertising channels, and messaging strategies that resonate with the characteristics and preferences of each segment.
• Monitoring and Iteration : Continuously monitor the performance of your marketing campaigns and customer segments. Refine your segments and marketing strategies as you gather more data and insights.
• Privacy and Compliance : Ensure that you handle customer data in compliance with privacy regulations, such as GDPR or CCPA, and prioritize data security throughout the process.

By effectively using data analytics to segment your customers, you can create more targeted and personalized marketing campaigns that are likely to yield better results and improve overall customer satisfaction.

A/B Testing Case Study

A social media platform wants to test a new feature to increase user engagement. Describe the steps you would take to design and analyze an A/B test to determine the impact of the new feature. What metrics would you track, and how would you interpret the results?

Designing and analyzing an A/B test for a new feature on a social media platform involves several critical steps. A well-executed A/B test can provide valuable insights into whether the new feature has a significant impact on user engagement. Here’s a step-by-step guide:

1. Define the Objective: Clearly define the objective of the A/B test. In this case, it’s to determine whether the new feature increases user engagement. Define what you mean by “user engagement” (e.g., increased time spent on the platform, higher interaction with posts, more shares, etc.).

2. Select the Test Group: Randomly select a representative sample of users from your platform. This will be your “test group.” Ensure that the sample size is statistically significant to detect meaningful differences.

3. Create Control and Test Groups: Divide the test group into two subgroups:

• Control Group (A): This group will not have access to the new feature.
• Test Group (B): This group will have access to the new feature.

4. Implement the Test: Implement the new feature for the Test Group while keeping the Control Group’s experience unchanged. Make sure that the user experience for both groups is consistent in all other aspects.

5. Measure Metrics: Define the metrics you will track to measure user engagement. Common metrics for social media platforms might include:

• Time spent on the platform
• Number of posts/comments/likes/shares
• User retention rate
• Click-through rate on recommended content

6. Collect Data: Run the A/B test for a predetermined period (e.g., one week or one month) to collect data on the selected metrics for both the Control and Test Groups.

7. Analyze the Results: Use statistical analysis to compare the metrics between the Control and Test Groups. Common techniques include:

• T-Tests : To compare means of continuous metrics like time spent on the platform.
• Chi-Square Tests : For categorical metrics like the number of shares.
• Cohort Analysis : To examine user behavior over time.

8. Interpret the Results: Interpret the results of the A/B test based on statistical significance and practical significance. Consider the following scenarios:

a. Statistically Significant Positive Results : If the new feature shows a statistically significant increase in user engagement, it may be a strong indicator that the feature positively impacts engagement.

b. Statistically Significant Negative Results : If the new feature shows a statistically significant decrease in user engagement, this suggests that the feature might have a negative impact, and you may need to reevaluate or iterate on the feature.

c. No Statistical Significance : If there’s no statistically significant difference between the Control and Test Groups, it’s inconclusive, and the new feature may not have a significant impact on user engagement.

9. Consider Secondary Metrics and User Feedback: Alongside primary metrics, consider secondary metrics and gather user feedback to gain a more comprehensive understanding of the new feature’s impact.

10. Make Informed Decisions: Based on the results, make informed decisions about whether to roll out the new feature to all users, iterate on the feature, or abandon it.

11. Monitor and Iterate: Continuously monitor user engagement metrics even after implementing the feature to ensure its long-term impact and make further improvements if necessary.

Remember that A/B testing is a powerful tool, but it’s important to ensure that your test design and statistical analysis are sound to draw accurate conclusions about the new feature’s impact on user engagement.

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Nailing An Analytics Interview Case Study: 10 Practical Strategies

10 practical tips/strategies I extracted myself when doing analytics case study as part of job interview process.

Jan 16, 2024 . 11 min read

Picture yourself aiming for coveted roles in the data realm, such as Senior Analytics Manager, Head of BI, Director of Analytics, and so on. If you aspire to leadership positions, you should be well versed in case studies - it is rigueur du jour in analytics interviews.

But what exactly makes a case study so vital? It's your stage to showcase how well you grasp a company's heartbeat: its business model. It's where your problem-solving, technical savvy, and ability to communicate like a seasoned team member come under the spotlight.

In this article, I will show you 10 strategies for acing your analytics interview case study.

To supplement this, I'm going to draw from my own real-life experiences. Specifically, I’ll be citing examples from my own experience interviewing for a tech giant in Singapore.

I’ve gone through my fair share of case studies and interviews with tech companies as a data professional with over a decade of experience. While I am by no means an expert, I hope these insights will inspire you to develop a personalized, winning approach to your next interview case study.

For this case study, I was asked to propose a method for mapping a large data set of Vietnamese addresses to geo coordinates in a cost-efficient and scalable manner.

• Input: A set of Vietnamese addresses in text form
• Output: For each address, their corresponding geo coordinates

I was also supplied with a dataset of 10,000+ Vietnamese addresses. But I can spare you the details here.

Above: Example of a Vietnamese address that needs to be mapped to a set of geocoordinates.

That’s the essence of the problem statement. Now let’s get into the 10 strategies/principles that I operate by.

Strategy 1: Show that you understand the context

Your first priority is to demonstrate that you understand the company’ business goals, its team dynamics, and the specific challenge at hand.

Above: My presentation begins with these slides, titled “The Challenge”, in which I distilled the problem into a clear, succinct statement, to show that I grasped the essence of the issue.

How I applied this strategy in my case study:

To prepare myself for this case study, I watched several videos on the company’s official YouTube channel so that I understood the company’s ambition of expanding into the Vietnamese market.

Next, I downloaded the product and tested it as a user, so that I’d get a firsthand perspective of how this data set would tie to the company’s product development framework.

Last but not least, I looked up the LinkedIn profiles of everyone on the interview panel to get a sense of their personalities and professional history. As the lead interviewer had a long history of working as a management consultant, I decided to craft my presentation as a set of PowerPoint slides, based on the assumption that this is the format that would be comfortable for a seasoned consultant.

This strategy wasn't just about the technicalities of the case study. It was about showing that I could fit into their world, understand their challenges, and speak their language.

Strategy 2: State your assumptions

Regardless of the problem you’ve been tasked to solve, you’re likely to have incomplete information, and will need to make a few reasonable assumptions - be it assumptions about the team’s intentions, the parameters of the problem, the desired solution, and so on.

This is equally true in the day-to-day reality of any professional environment; decision-making is rarely black and white. A good leader, however, is able to anticipate knowledge gaps and exercise good judgment in the face of it. The case study is your opportunity to showcase these crucial skills.

Above: The first of a few slides in which I stated the assumptions I made before tackling the problem.

In my case study, I listed assumptions that I’d made regarding the technical details of the problem, the long-term applicability of a desired solution, as well as the expected timeline for solving the problem.

None of these factors were addressed in my assignment. However, given that they’d dramatically restrict the possibilities of a viable solution, I felt that it would be wise to sketch out these areas of uncertainty. By doing so, I was able to apply reasonable conjectures and zoom in on a practical solution.

Strategy 3: Explain your thought process

This is an important point that you must remember: Case studies are less about pinpointing a specific solution, and more about unveiling the narrative of your problem-solving style. Interviewers are keen to dive into your thought process, to see how you navigate a maze of challenges, rather than just where you end up.

Above: The slide in which I not only stated my proposed solution (using HERE Location Services), but also the thought processes that guided my approach.

In my case study, I ultimately proposed using HERE Location Services for mapping Vietnamese addresses to geocoordinates.

How did I arrive at this solution? It began with a careful weighing of goals, like balancing accuracy against cost-efficiency, and taking constraints (such as budgets) into account.

Next, I conducted a comparative analysis between HERE Location Services vs. other possibilities. I highlighted the superior quality of HERE Location Services’s data sources compared to most of its competitors, as well as its attractive pricing model, thereby presenting a compelling case for my choice.

Moreover, I leveraged my past experiences, drawing parallels between this case study and similar projects I had undertaken previously. On another slide, I detailed how these experiences provided a rich backdrop to my current approach, adding depth and credibility to my solution.

Strategy 4: Validate your solution

As you lay out a solution, it is important that it doesn’t just sound good on paper - it needs to stand up to real-world scrutiny and application.

A good solution is one that meets redefined objectives and creates value, be it in terms of cost-efficiency, time savings, improved health outcomes, increased customer satisfaction, or any other metric that’s relevant to the company’s product model.

Try to answer this question: If your approach is a good one, how would its success be measured?

Above: The slide in which I propose a method for validating my own proposed solution, i.e. benchmarking HERE Location Services against Google Maps.

In my case study, I proposed using Google Maps Geocoding as the industry gold standard, and the following as a criteria for success: If X service is a reliable solution, then it should be able to mirror Google Maps Geocoding’s results with only a small loss in accuracy.

Next, I created a trial account on HERE Location services and tested a small sample data set of Vietnamese addresses, and demonstrated that it was, indeed, able to replicate Google Maps Geocoding reliably. In doing so, I didn’t just propose a solution, I also proved its viability in the real world.

Strategy 5: Anticipate, adapt, and articulate

The climax of your case study is not how you present your solution, but how you defend it from a barrage of questions from your interviewers. To navigate this smoothly, you can take a pre-emptive approach by anticipating these questions and integrating the answers in your presentation, showcasing not just your solution’s strength, but your foresight as well.

Above: I anticipated several scenarios in which my solution might evolve or require scaling the future. For instance, I anticipated that the company may want to expand into new markets beyond Vietnam, and replicate the same geo-mapping exercise in new markets.

So, how did I turn this anticipation into an asset during my case study? I prepared myself for a range of questions, such as:

• What are the potential hiccups and roadblocks of your solution?
• Let’s say that the business goal / scope of the problem shifted unexpectedly, how would you tailor your plans?
• What kind of support would you need from us to implement your solution?

As it turned out, many of these questions did come up during the interview.

But let's be real – no matter how well you prepare, there will always be curveballs. Whenever the panel threw a question I hadn’t foreseen, I stayed grounded. I would respond, "In a real-world scenario, I'd take some time to consult with experts like ABC and delve into research on topics like XYZ to formulate a well-rounded hypothesis."

This approach served a dual purpose. It showed that I could think on my feet and, more importantly, that I understood the value of thorough research and collaboration in tackling unforeseen challenges. This way, even without an immediate answer, I demonstrated a methodical and strategic approach to problem-solving."

Strategy 6: Add depth to your presentation with an appendix

As you draw your presentation to a close, consider the impact of an appendix. This section can be a treasure trove of supplementary details, showcasing the depth and rigor of your preparation. Many interviewers will be impressed by this extra effort, seeing it as a testament to your thoroughness and commitment to providing a comprehensive, informative deck.

Above: I added slides in which I explained how I approached my case study.

In my case study, I decided to enrich my presentation with a detailed appendix. Here’s what I included:

• A Peek Behind the Curtain: I provided snapshots on how I prepared for the case study, including people from whom I solicited feedback, tools and resources I’d used, etc.
• Technical Documentation: I provided the actual Python scripts and calculations that I used to answer technical questions, to serve as concrete evidence of my analytical capabilities.
• Notes On the Complexity of Vietnamese Addresses: I dedicated a section to elaborate on the complexities of mapping Vietnamese addresses. This wasn't just about showing the problem; it was about highlighting the nuanced understanding I had developed regarding this specific challenge.

Strategy 7: Elevate your presentation with good visual design

While it's the content that truly matters, never underestimate the power of a visually captivating presentation. It's the icing on the cake that can set you apart from other candidates.

Above: I like to enhance my presentation with beautiful images and photos from royalty-free sources such as Unsplash.

The following are some of the stylistic practices that I personally use in almost all of my interview presentations:

• Embrace the Company’s Visual Identity: I love to align my presentation with the company's branding. Using their official fonts and color palette not only shows that I've done my homework but also helps my presentation resonate with the company's ethos.
• Legibility is Key : Dense paragraphs are a no-go. I keep my text concise, aiming for a maximum of 2-3 sentences per paragraph. If the text starts to get lengthy, I break it up over multiple slides. It's all about making the content digestible and easy on the eyes.
• Consistency is Crucia l: From font sizes to text box positions and paragraph styles, I ensure every visual element tells a unified story. This consistency underscores the narrative of my presentation, making it more compelling and professional.
• Strategic Use of Images : To break the monotony of text, I sprinkle in high-resolution, royalty-free images from sources like Unsplash. These images aren't just fillers; they're carefully selected to enhance the narrative and add a visual punch.
• Smart URL Customization : When I use browser-based presentation tools like Google Slides or Miro, I create custom URLs for easy access. For instance, transforming a lengthy link into something sleek like www.tinyurl.com/holisticscasestudy not only makes it more memorable but also adds a layer of professionalism.

Through these subtle yet impactful design choices, I aim to convey meticulousness, consistency, and a work ethic that values thoughtfulness and rigor.

Strategy 8: Refine and rehearse

After drafting your presentation, it's time to elevate it from good to great:

Seek insightful feedback: Share a duplicate of your presentation with trusted friends or mentors. Their fresh perspectives can provide invaluable insights on how to enhance your presentation.

Master the delivery: Rehearse, rehearse, and rehearse some more. Whether it's with a partner or recording yourself, this step is crucial. You've invested hours in the content; now, focus on how you deliver it. Aim for clarity, structure, and a compelling narrative that keeps your audience hooked.

One more tip: Always start with a brief introduction about yourself; don’t assume that all your interviewers know who you are. It helps to set the stage before you dive into your presentation.

Strategy 9: Mind the clock

On the big day, keep an eye on the clock. Even with the most meticulous preparation, you might face unexpected technical hiccups and delays. A good rule of thumb is to aim to complete your presentation within 80% of the allotted time. For instance, if you have 30 minutes, try wrapping up around the 24-25 minute mark.

During the Q&A session, if given the option, always choose to address questions at the end. This keeps your presentation flow uninterrupted and ensures that your audience hears your complete thoughts before they jump into questions.

Strategy 10: Treat the interview as a two-way street

Remember, the case study is as much about you evaluating the company as it is about them evaluating you. Use this opportunity to ask insightful questions about the team, upcoming projects, and the rationale behind the case study. This dialogue will give you a clear picture of the company's values and work culture.

Post-interview reflections are just as crucial. Ask yourself: Can you see yourself thriving in this environment?

Interviewers from an organization with good work culture will always ask questions in a respectful manner, and provide constructive feedback. The nature of your interactions can provide valuable insight into the kind of support, mentorship, and collaboration you can expect if you join the company.

Full disclosure: Despite my efforts, I didn’t land the job for which I crafted the attached case study. Nevertheless, I still had fun and learned something new in the process of doing research. Case studies, while demanding, have always been the highlight of my interviews.

Regardless of the outcome, treat every case study as a learning experience - as a way to learn more about different companies, product problems, and business strategies, and get better at interviewing. The hours that you spend chipping away at challenges like these are a vital part of your career development. Maybe the real treasure is the insights we gain along the way. ;)

p/s: You can find the complete slides here www.tinyurl.com/holisticscasestudy (company name removed for obvious reasons).

For more practical blog posts like this one, check out:

• The skills chasm of the data analyst career
• Data analysts, think about your work from the business stakeholders perspective
• The Misleading Data Analyst Job Title (and Career Ladder)

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Data Science Case Study Interview: Your Guide to Success

by Sam McKay, CFA | Careers

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

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

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

To master your data science case study interview:

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

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

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

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

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

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

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

Let’s dive in!

Table of Contents

What to Expect in the Interview?

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

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

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

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

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

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

Topic 1: Problem-Solving Scenarios

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

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

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

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

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

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

Topic 2: Data Handling and Analysis

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

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

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

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

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

Topic 3: Modeling and Feature Selection

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

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

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

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

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

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

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

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

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

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

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

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

Topic 5: Evaluation Metrics and Validation

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

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

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

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

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

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

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

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

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

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

Steps by Step Guide Through the Interview

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

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

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

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

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

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

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

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

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

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

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

Top 3 Tips to Master Your Data Science Case Study Interview

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

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

1. Comprehensive Preparation Tips

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

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

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

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

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

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

2. Communication and Presentation of Results

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

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

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

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

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

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

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

3. Structured Preparation Strategy

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

Here’s how:

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

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

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

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

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

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

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

Now, let’s really make this specific.

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

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

Case Interviews at Top Tech Companies

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

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

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

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

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

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

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

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

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

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

Mockup Case Studies and Practice Questions

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

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

Here are a few examples to help you get started:

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

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

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

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

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

Final Thoughts

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Data Science 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?

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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.

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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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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.

Explore Categories

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.

About the Author

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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47 case interview examples (from McKinsey, BCG, Bain, etc.)

One of the best ways to prepare for   case interviews  at firms like McKinsey, BCG, or Bain, is by studying case interview examples. 

There are a lot of free sample cases out there, but it's really hard to know where to start. So in this article, we have listed all the best free case examples available, in one place.

The below list of resources includes interactive case interview samples provided by consulting firms, video case interview demonstrations, case books, and materials developed by the team here at IGotAnOffer. Let's continue to the list.

• McKinsey examples
• BCG examples
• Bain examples
• Deloitte examples
• Other firms' examples
• Case books from consulting clubs
• Case interview preparation

Click here to practise 1-on-1 with MBB ex-interviewers

1. mckinsey case interview examples.

• Beautify case interview (McKinsey website)
• Diconsa case interview (McKinsey website)
• Electro-light case interview (McKinsey website)
• GlobaPharm case interview (McKinsey website)
• National Education case interview (McKinsey website)
• Talbot Trucks case interview (McKinsey website)
• Shops Corporation case interview (McKinsey website)
• Conservation Forever case interview (McKinsey website)
• McKinsey case interview guide (by IGotAnOffer)
• McKinsey live case interview extract (by IGotAnOffer) - See below

2. BCG case interview examples

• Foods Inc and GenCo case samples  (BCG website)
• Chateau Boomerang written case interview  (BCG website)
• BCG case interview guide (by IGotAnOffer)
• Written cases guide (by IGotAnOffer)
• BCG live case interview with notes (by IGotAnOffer)
• BCG mock case interview with ex-BCG associate director - Public sector case (by IGotAnOffer)
• BCG mock case interview: Revenue problem case (by IGotAnOffer) - See below

3. Bain case interview examples

• CoffeeCo practice case (Bain website)
• FashionCo practice case (Bain website)
• Associate Consultant mock interview video (Bain website)
• Consultant mock interview video (Bain website)
• Written case interview tips (Bain website)
• Bain case interview guide   (by IGotAnOffer)
• Digital transformation case with ex-Bain consultant
• Bain case mock interview with ex-Bain manager (below)

4. Deloitte case interview examples

• Engagement Strategy practice case (Deloitte website)
• Recreation Unlimited practice case (Deloitte website)
• Strategic Vision practice case (Deloitte website)
• Retail Strategy practice case  (Deloitte website)
• Finance Strategy practice case  (Deloitte website)
• Talent Management practice case (Deloitte website)
• Enterprise Resource Management practice case (Deloitte website)
• Footloose written case  (by Deloitte)
• Deloitte case interview guide (by IGotAnOffer)

5. Accenture case interview examples

• Case interview workbook (by Accenture)
• Accenture case interview guide (by IGotAnOffer)

6. OC&C case interview examples

• Leisure Club case example (by OC&C)
• Imported Spirits case example (by OC&C)

7. Oliver Wyman case interview examples

• Wumbleworld case sample (Oliver Wyman website)
• Aqualine case sample (Oliver Wyman website)
• Oliver Wyman case interview guide (by IGotAnOffer)

8. A.T. Kearney case interview examples

• Promotion planning case question (A.T. Kearney website)
• Consulting case book and examples (by A.T. Kearney)
• AT Kearney case interview guide (by IGotAnOffer)

9. Strategy& / PWC case interview examples

• Presentation overview with sample questions (by Strategy& / PWC)
• Strategy& / PWC case interview guide (by IGotAnOffer)

10. L.E.K. Consulting case interview examples

• Case interview example video walkthrough   (L.E.K. website)
• Market sizing case example video walkthrough  (L.E.K. website)

11. Roland Berger case interview examples

• Transit oriented development case webinar part 1  (Roland Berger website)
• Transit oriented development case webinar part 2   (Roland Berger website)
• 3D printed hip implants case webinar part 1   (Roland Berger website)
• 3D printed hip implants case webinar part 2   (Roland Berger website)
• Roland Berger case interview guide   (by IGotAnOffer)

12. Capital One case interview examples

• Case interview example video walkthrough  (Capital One website)
• Capital One case interview guide (by IGotAnOffer)

13. Consulting clubs case interview examples

• Berkeley case book (2006)
• Columbia case book (2006)
• Darden case book (2012)
• Darden case book (2018)
• Duke case book (2010)
• Duke case book (2014)
• ESADE case book (2011)
• Goizueta case book (2006)
• Illinois case book (2015)
• LBS case book (2006)
• MIT case book (2001)
• Notre Dame case book (2017)
• Ross case book (2010)
• Wharton case book (2010)

Practice with experts

Using case interview examples is a key part of your interview preparation, but it isn’t enough.

At some point you’ll want to practise with friends or family who can give some useful feedback. However, if you really want the best possible preparation for your case interview, you'll also want to work with ex-consultants who have experience running interviews at McKinsey, Bain, BCG, etc.

If you know anyone who fits that description, 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 do mock case interviews 1-on-1 with ex-interviewers from MBB firms . Start scheduling sessions today!

The IGotAnOffer team

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Nonlinearity:

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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[2023] Meta Data Science Interview (+ Case Examples)

Aspiring to become a data scientist at Meta? A core pillar within the data science at Meta is the analytics role specialized in data analysis, data visualization, and AB testing. And, you have the opportunity to work across various products in the Meta ecosystem - Facebook, Instagram, Messenger, WhatsApp, Thread, AR/VR Devices .

Let’s look at a detailed guide on how to ACE the data scientist interview at Meta . Here are 7 key aspects to consider as you prepare for the data scientist interview.

• 📝 Job Application
• ⏰ Interview Process - Recruiter Screen/Technical Screen/Onsite Interviews
• ✍️ Example Questions
• 💡 Preparation Tips

1. Job Application

Getting your application spotted by a recruiter at Meta is tricky. There are, however, a number of strategies you can execute to maximize your chance of landing an interview.

1.1 Understand the role expectation

Meta has the following expectations about the role of the data scientist. Understanding their expectations provide clues on how the interviews will be structured in technical and behavioral rounds.

• Explore large data sets to provide actionable insights with data visualizations
• Track product health and conduct experiment design & analysis
• Partner with data engineers on tables, dashboards, metrics, and goals
• Design robust experiments, considering statistical and practical significance and biases

Soft Skills

• Partner with cross-functional teams to inform and influence product roadmap and go-to-market strategies
• Apply expertise in quantitative analysis and data mining to develop data-informed strategies for improving products.
• Drive product decisions via actionable insights and data storytelling

You will see later, based on actual question examples, Meta places a great deal of assessing the candidate’s competencies in data preparation/analysis, product sense, experimentation, and stakeholder communication .

2.2 Tailor your resume

Tailor your resume to highlight the background and skills that recruiters and hiring managers look for:

• Bachelor's degree (Master’s preferred) in Mathematics, Statistics, and a relevant technical field
• Work experience in analytics and data science specialized in product
• Experience with SQL, Python, R, or other programming languages.
• Proven executive-level communication skills to influence product decisions.

2. Interview Process

The interview process at Meta can take 4 to 8 weeks. In some cases the entire process is expedited if you have a competing offer from another FAANG company (e.g. Google). The steps are recruiter screen → technical screen → onsite interview.

2.1 Recruiter Screen

The recruiter screen at Meta is usually formatted the following way:

• 📝 Format: Phone Call
• ⏰ Duration: 20 to 30 minutes
• 💭 Interviewer: Technical Recruiter
• 📚 Questions: Behavioral, Culture-Fit, Logistics

In the meeting expect to discuss the following:

• On Meta’s mission and About the Role
• Your Background - - "Walk me through your resume. Why do you want to work at Meta?”
• Light Technical Questions - In some cases, a recruiter may ask simple statistics or SQL questions like explain the difference between INNER/LEFT/OUTER JOINS.
• Your Logistics - Expect to discuss your visa/citizenship status, remote/hybrid/location preference, scheduling for the next interview.

Pro Tip💡 - Practice explaining your story prior to the interview

2.2 Technical Screen

The technical screen at Meta is usually conducted on Coderpad, or a virtual pad where the interviewer will assess your coding and product sense ability.

• 📝 Format: Video Conference Call
• ⏰ Duration: 45 to 60 minutes
• 💭 Interviewer: Senior/Staff DS
• 📚 Questions: Data Manipulation (using SQL or coding) and Product Case

Pro Tip💡 - Practice 2 to 3 data manipulation problems up front when you are starting preparation with Meta. Aim to crack a problem within the 7 to 8 minute time limit.

2.3 Onsite Interview

1 to 3 weeks after the technical screen, you will be scheduled for the onsite stage. This is the most challenging aspect of the interview process. The bar is much higher than the technical screen.

• 📝 Format: Video Conference Calls
• 💭 Interviewer: Senior/Staff DS or Data Science Manager
• 📚 Rounds/Questions: 4 to 5 Rounds - Programming, Research Design, Metrics, Data Analysis, Behavioral

Pro Tip💡 - Continue to ramp up on data manipulation skills and practice case problems verbally.

3. Interview Questions

Throughout the interview process, you will be assessed on a combination of the following areas:

📚  Areas Covered

• Programming
• Research Design
• Determining Goals and Success Metrics
• Data Analysis

3.1 Programming

The interviewer will assess you on on your familiarity with data manipulations (e.g. merging datasets, filtering data to find insights). Expect to discuss trade-offs in coding/query efficiency. Things to consider:

• You will be given a choice of language to solve the problem - SQL, Python, R
• It doesn’t matter which SQL (e.g. MySQL, PostgresQL) you are using as long as you know the common syntax
• Explain your solution verbally as you write and/or after you are done.

📝 Here’s an actual question…

3.2 Research Design

Your interviewer is assessing design and explanation of AB testing and/or causal inference in various product cases. The general form of this question is formatted as follows:

• Suppose we want to change feature [X] how would you design an experiment and test whether to change the feature or not?
• What are the downsides of the methodology you propose? Are there biases in the analysis or experiment that we should correct for?

The Messenger team proposes a feature that enables users to receive recent messages either unread or unresponded. How would you measure the effectiveness of this feature in an experiment?

👇 Here’s the solution

3.3 Determining Goals and Success Metrics

Your interviewer wants to see your ability to define metrics that reflect success and inform business objectives. The question is usually formatted the following way: How would you measure [X] of a product [Y]? [X] is a quality like success, health, satisfaction; [Y] is any feature or product of Meta like Feed, Notifications, Instagram, and WhatsApp.

How would you set goals and measure success for Facebook notifications?

3.4 Data Analysis

Your interviewer will be looking for how you leverage methods ranging from descriptive statistics to statistical models to answer hypothesis-driven questions. Things to consider are the following:

• What are the hypotheses that would lead to a decision? How would you prove a hypothesis is true?
• Can you translate concepts generated into a specific analysis plan? Are you able to use data to answer the original question posed with enough detail to demonstrate the ability to execute on an analysis?

How would you measure the impact of parents being on Facebook on teenagers?

4. Preparation Tips

Use the following resources to further help your prep!

• Read the Meta’s Financials and KPIs
• Visit the Meta’s Engineering Blog
• Practice SQL Problem Sets
• Join the Data Science Interview Bootcamp led by FAANG Data Scientists/Interviewers

Tutorial Playlist

Data analytics tutorial for beginners: a step-by-step guide, what is data analytics and its future scope in 2024, data analytics with python: use case demo, all the ins and outs of exploratory data analysis, top 5 business intelligence tools, the ultimate guide to qualitative vs. quantitative research.

How to Become a Data Analyst: A Step-by-Step Guide

Data Analyst vs. Data Scientist: The Ultimate Comparison

Top 60 Data Analyst Interview Questions and Answers for 2024

Understanding the fundamentals of confidence interval in statistics, applications of data analytics: real-world applications and impact, 66 data analyst interview questions to ace your interview.

Lesson 8 of 10 By Shruti M

Table of Contents

Data analytics is widely used in every sector in the 21st century. A career in the field of data analytics is highly lucrative in today's times, with its career potential increasing by the day. Out of the many job roles in this field, a data analyst's job role is widely popular globally. A data analyst collects and processes data; he/she analyzes large datasets to derive meaningful insights from raw data. 

Your Data Analytics Career is Around The Corner!

General Data Analyst Interview Questions

In an interview, these questions are more likely to appear early in the process and cover data analysis at a high level. 

1. Mention the differences between Data Mining and Data Profiling?

2. define the term 'data wrangling in data analytics..

Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze the data. Thereafter it gets ready to be used with another dataset.

3. What are the various steps involved in any analytics project?

This is one of the most basic data analyst interview questions. The various steps involved in any common analytics projects are as follows:

Understanding the Problem

Understand the business problem, define the organizational goals, and plan for a lucrative solution.

Collecting Data

Gather the right data from various sources and other information based on your priorities.

Cleaning Data

Clean the data to remove unwanted, redundant, and missing values, and make it ready for analysis.

Exploring and Analyzing Data

Use data visualization and business intelligence tools , data mining techniques, and predictive modeling to analyze data.

Interpreting the Results

Interpret the results to find out hidden patterns, future trends, and gain insights.

4. What are the common problems that data analysts encounter during analysis?

The common problems steps involved in any analytics project are:

• Handling duplicate 
• Collecting the meaningful right data and the right time
• Handling data purging and storage problems
• Making data secure and dealing with compliance issues

5. Which are the technical tools that you have used for analysis and presentation purposes?

As a data analyst , you are expected to know the tools mentioned below for analysis and presentation purposes. Some of the popular tools you should know are:

MS SQL Server, MySQL

For working with data stored in relational databases

MS Excel, Tableau

For creating reports and dashboards

Python, R, SPSS

For statistical analysis, data modeling, and exploratory analysis

MS PowerPoint

For presentation, displaying the final results and important conclusions 

6. What are the best methods for data cleaning?

• Create a data cleaning plan by understanding where the common errors take place and keep all the communications open.
• Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process .
• Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.
• Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.

7. What is the significance of Exploratory Data Analysis (EDA)?

• Exploratory data analysis (EDA) helps to understand the data better.
• It helps you obtain confidence in your data to a point where you’re ready to engage a machine learning algorithm.
• It allows you to refine your selection of feature variables that will be used later for model building.
• You can discover hidden trends and insights from the data.

8. Explain descriptive, predictive, and prescriptive analytics.

9. what are the different types of sampling techniques used by data analysts.

Sampling is a statistical method to select a subset of data from an entire dataset (population) to estimate the characteristics of the whole population. 

There are majorly five types of sampling methods:

• Simple random sampling
• Systematic sampling
• Cluster sampling
• Stratified sampling
• Judgmental or purposive sampling

10. Describe univariate, bivariate, and multivariate analysis.

Univariate analysis is the simplest and easiest form of data analysis where the data being analyzed contains only one variable. 

Example - Studying the heights of players in the NBA.

Univariate analysis can be described using Central Tendency, Dispersion, Quartiles, Bar charts, Histograms, Pie charts, and Frequency distribution tables.

The bivariate analysis involves the analysis of two variables to find causes, relationships, and correlations between the variables. 

Example – Analyzing the sale of ice creams based on the temperature outside.

The bivariate analysis can be explained using Correlation coefficients, Linear regression, Logistic regression, Scatter plots, and Box plots.

The multivariate analysis involves the analysis of three or more variables to understand the relationship of each variable with the other variables. 

Example – Analysing Revenue based on expenditure.

Multivariate analysis can be performed using Multiple regression, Factor analysis, Classification & regression trees, Cluster analysis, Principal component analysis, Dual-axis charts, etc.

11. What are your strengths and weaknesses as a data analyst?

The answer to this question may vary from a case to case basis. However, some general strengths of a data analyst may include strong analytical skills, attention to detail, proficiency in data manipulation and visualization, and the ability to derive insights from complex datasets. Weaknesses could include limited domain knowledge, lack of experience with certain data analysis tools or techniques, or challenges in effectively communicating technical findings to non-technical stakeholders.

12. What are the ethical considerations of data analysis?

Some of the most the ethical considerations of data analysis includes:

• Privacy: Safeguarding the privacy and confidentiality of individuals' data, ensuring compliance with applicable privacy laws and regulations.
• Informed Consent: Obtaining informed consent from individuals whose data is being analyzed, explaining the purpose and potential implications of the analysis.
• Data Security: Implementing robust security measures to protect data from unauthorized access, breaches, or misuse.
• Data Bias: Being mindful of potential biases in data collection, processing, or interpretation that may lead to unfair or discriminatory outcomes.
• Transparency: Being transparent about the data analysis methodologies, algorithms, and models used, enabling stakeholders to understand and assess the results.
• Data Ownership and Rights: Respecting data ownership rights and intellectual property, using data only within the boundaries of legal permissions or agreements.
• Accountability: Taking responsibility for the consequences of data analysis, ensuring that actions based on the analysis are fair, just, and beneficial to individuals and society.
• Data Quality and Integrity: Ensuring the accuracy, completeness, and reliability of data used in the analysis to avoid misleading or incorrect conclusions.
• Social Impact: Considering the potential social impact of data analysis results, including potential unintended consequences or negative effects on marginalized groups.
• Compliance: Adhering to legal and regulatory requirements related to data analysis, such as data protection laws, industry standards, and ethical guidelines.

13. What are some common data visualization tools you have used?

You should name the tools you have used personally, however here’s a list of the commonly used data visualization tools in the industry:

• Microsoft Power BI
• Google Data Studio
• Matplotlib (Python library)
• Excel (with built-in charting capabilities)
• IBM Cognos Analytics

Data Analyst Interview Questions On Statistics

14. how can you handle missing values in a dataset.

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset.

Listwise Deletion

In the listwise deletion method, an entire record is excluded from analysis if any single value is missing.

Average Imputation 

Take the average value of the other participants' responses and fill in the missing value.

Regression Substitution

You can use multiple-regression analyses to estimate a missing value.

Multiple Imputations

It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions.

15. Explain the term Normal Distribution.

Normal Distribution refers to a continuous probability distribution that is symmetric about the mean. In a graph, normal distribution will appear as a bell curve.

• The mean, median, and mode are equal
• All of them are located in the center of the distribution
• 68% of the data falls within one standard deviation of the mean
• 95% of the data lies between two standard deviations of the mean
• 99.7% of the data lies between three standard deviations of the mean

16. What is Time Series analysis?

Time Series analysis is a statistical procedure that deals with the ordered sequence of values of a variable at equally spaced time intervals. Time series data are collected at adjacent periods. So, there is a correlation between the observations. This feature distinguishes time-series data from cross-sectional data.

Below is an example of time-series data on coronavirus cases and its graph.

17. How is Overfitting different from Underfitting?

This is another frequently asked data analyst interview question, and you are expected to cover all the given differences!

18. How do you treat outliers in a dataset? 

An outlier is a data point that is distant from other similar points. They may be due to variability in the measurement or may indicate experimental errors. 

The graph depicted below shows there are three outliers in the dataset.

To deal with outliers, you can use the following four methods:

• Drop the outlier records
• Cap your outliers data
• Assign a new value
• Try a new transformation

19. What are the different types of Hypothesis testing?

Hypothesis testing is the procedure used by statisticians and scientists to accept or reject statistical hypotheses. There are mainly two types of hypothesis testing:

• Null hypothesis : It states that there is no relation between the predictor and outcome variables in the population. H0 denoted it.  

Example: There is no association between a patient’s BMI and diabetes.

• Alternative hypothesis : It states that there is some relation between the predictor and outcome variables in the population. It is denoted by H1.

Example: There could be an association between a patient’s BMI and diabetes.

20. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

21. How would you handle missing data in a dataset?

Ans: The choice of handling technique depends on factors such as the amount and nature of missing data, the underlying analysis, and the assumptions made. It's crucial to exercise caution and carefully consider the implications of the chosen approach to ensure the integrity and reliability of the data analysis. However, a few solutions could be:

• removing the missing observations or variables
• imputation methods including, mean imputation (replacing missing values with the mean of the available data), median imputation (replacing missing values with the median), or regression imputation (predicting missing values based on regression models)
• sensitivity analysis 

22. Explain the concept of outlier detection and how you would identify outliers in a dataset.

Outlier detection is the process of identifying observations or data points that significantly deviate from the expected or normal behavior of a dataset. Outliers can be valuable sources of information or indications of anomalies, errors, or rare events.

It's important to note that outlier detection is not a definitive process, and the identified outliers should be further investigated to determine their validity and potential impact on the analysis or model. Outliers can be due to various reasons, including data entry errors, measurement errors, or genuinely anomalous observations, and each case requires careful consideration and interpretation.

Excel Data Analyst Interview Questions

23. in microsoft excel, a numeric value can be treated as a text value if it precedes with what.

24. What is the difference between COUNT, COUNTA, COUNTBLANK, and COUNTIF in Excel?

• COUNT function returns the count of numeric cells in a range
• COUNTA function counts the non-blank cells in a range
• COUNTBLANK function gives the count of blank cells in a range
• COUNTIF function returns the count of values by checking a given condition

Future-Proof Your AI/ML Career: Top Dos and Don'ts

25. How do you make a dropdown list in MS Excel?

• First, click on the Data tab that is present in the ribbon.
• Under the Data Tools group, select Data Validation.
• Then navigate to Settings > Allow > List.
• Select the source you want to provide as a list array.

26. Can you provide a dynamic range in “Data Source” for a Pivot table?

Yes, you can provide a dynamic range in the “Data Source” of Pivot tables. To do that, you need to create a named range using the offset function and base the pivot table using a named range constructed in the first step.

27. What is the function to find the day of the week for a particular date value?

The get the day of the week, you can use the WEEKDAY() function.

The above function will return 6 as the result, i.e., 17th December is a Saturday.

28. How does the AND() function work in Excel?

AND() is a logical function that checks multiple conditions and returns TRUE or FALSE based on whether the conditions are met.

Syntax: AND(logica1,[logical2],[logical3]....)

In the below example, we are checking if the marks are greater than 45. The result will be true if the mark is >45, else it will be false.

29. Explain how VLOOKUP works in Excel?

VLOOKUP is used when you need to find things in a table or a range by row.

VLOOKUP accepts the following four parameters:

lookup_value - The value to look for in the first column of a table

table - The table from where you can extract value

col_index - The column from which to extract value

range_lookup - [optional] TRUE = approximate match (default). FALSE = exact match

Let’s understand VLOOKUP with an example.

If you wanted to find the department to which Stuart belongs to, you could use the VLOOKUP function as shown below:

Here, A11 cell has the lookup value, A2:E7 is the table array, 3 is the column index number with information about departments, and 0 is the range lookup. 

If you hit enter, it will return “Marketing”, indicating that Stuart is from the marketing department.

30. What function would you use to get the current date and time in Excel?

In Excel, you can use the TODAY() and NOW() function to get the current date and time.

31. Using the below sales table, calculate the total quantity sold by sales representatives whose name starts with A, and the cost of each item they have sold is greater than 10.

You can use the SUMIFS() function to find the total quantity.

For the Sales Rep column, you need to give the criteria as “A*” - meaning the name should start with the letter “A”. For the Cost each column, the criteria should be “>10” - meaning the cost of each item is greater than 10.

The result is 13 .

33. Using the data given below, create a pivot table to find the total sales made by each sales representative for each item. Display the sales as % of the grand total.

• Select the entire table range, click on the Insert tab and choose PivotTable

• Select the table range and the worksheet where you want to place the pivot table

• Drag Sale total on to Values, and Sales Rep and Item on to Row Labels. It will give the sum of sales made by each representative for every item they have sold.

• Right-click on “Sum of Sale Total’ and expand Show Values As to select % of Grand Total.

• Below is the resultant pivot table.

SQL Interview Questions for Data Analysts

34. how do you subset or filter data in sql.

To subset or filter data in SQL, we use WHERE and HAVING clauses.

Consider the following movie table.

Using this table, let’s find the records for movies that were directed by Brad Bird.

Now, let’s filter the table for directors whose movies have an average duration greater than 115 minutes.

35. What is the difference between a WHERE clause and a HAVING clause in SQL?

Answer all of the given differences when this data analyst interview question is asked, and also give out the syntax for each to prove your thorough knowledge to the interviewer.

Syntax of WHERE clause:

SELECT column1, column2, ... FROM table_name WHERE condition;

Syntax of HAVING clause;

SELECT column_name(s) FROM table_name WHERE condition GROUP BY column_name(s) HAVING condition ORDER BY column_name(s);

36. Is the below SQL query correct? If not, how will you rectify it?

The query stated above is incorrect as we cannot use the alias name while filtering data using the WHERE clause. It will throw an error.

37. How are Union, Intersect, and Except used in SQL?

The Union operator combines the output of two or more SELECT statements.

SELECT column_name(s) FROM table1 UNION SELECT column_name(s) FROM table2;

Let’s consider the following example, where there are two tables - Region 1 and Region 2.

To get the unique records, we use Union.

The Intersect operator returns the common records that are the results of 2 or more SELECT statements.

SELECT column_name(s) FROM table1 INTERSECT SELECT column_name(s) FROM table2;

The Except operator returns the uncommon records that are the results of 2 or more SELECT statements.

SELECT column_name(s) FROM table1 EXCEPT SELECT column_name(s) FROM table2;

Below is the SQL query to return uncommon records from region 1.

38. What is a Subquery in SQL?

A Subquery in SQL is a query within another query. It is also known as a nested query or an inner query. Subqueries are used to enhance the data to be queried by the main query. 

It is of two types - Correlated and Non-Correlated Query.

Below is an example of a subquery that returns the name, email id, and phone number of an employee from Texas city.

SELECT name, email, phone

FROM employee

WHERE emp_id IN (

SELECT emp_id

WHERE city = 'Texas');

39. Using the product_price table, write an SQL query to find the record with the fourth-highest market price.

Fig: Product Price table

select top 4 * from product_price order by mkt_price desc;

Now, select the top one from the above result that is in ascending order of mkt_price.

40. From the product_price table, write an SQL query to find the total and average market price for each currency where the average market price is greater than 100, and the currency is in INR or AUD.

The SQL query is as follows:

The output of the query is as follows:

41. Using the product and sales order detail table, find the products with total units sold greater than 1.5 million.

Fig: Products table

Fig: Sales order detail table

We can use an inner join to get records from both the tables. We’ll join the tables based on a common key column, i.e., ProductID.

The result of the SQL query is shown below.

42. How do you write a stored procedure in SQL ?

You must be prepared for this question thoroughly before your next data analyst interview. The stored procedure is an SQL script that is used to run a task several times.

Let’s look at an example to create a stored procedure to find the sum of the first N natural numbers' squares.

• Create a procedure by giving a name, here it’s squaresum1
• Declare the variables
• Write the formula using the set statement
• Print the values of the computed variable
• To run the stored procedure, use the EXEC command

Output: Display the sum of the square for the first four natural numbers

43. Write an SQL stored procedure to find the total even number between two users given numbers.

Here is the output to print all even numbers between 30 and 45.

Tableau Data Analyst Interview Questions

44. how is joining different from blending in tableau.

45. What do you understand by LOD in Tableau?

LOD in Tableau stands for Level of Detail. It is an expression that is used to execute complex queries involving many dimensions at the data sourcing level. Using LOD expression, you can find duplicate values, synchronize chart axes and create bins on aggregated data.

46. Can you discuss the process of feature selection and its importance in data analysis?

Feature selection is the process of selecting a subset of relevant features from a larger set of variables or predictors in a dataset. It aims to improve model performance, reduce overfitting, enhance interpretability, and optimize computational efficiency. Here's an overview of the process and its importance:

Importance of Feature Selection:

- Improved Model Performance: By selecting the most relevant features, the model can focus on the most informative variables, leading to better predictive accuracy and generalization. - Overfitting Prevention: Including irrelevant or redundant features can lead to overfitting, where the model learns noise or specific patterns in the training data that do not generalize well to new data. Feature selection mitigates this risk. - Interpretability and Insights: A smaller set of selected features makes it easier to interpret and understand the model's results, facilitating insights and actionable conclusions. - Computational Efficiency: Working with a reduced set of features can significantly improve computational efficiency, especially when dealing with large datasets.

47. What are the different connection types in Tableau Software?

There are mainly 2 types of connections available in Tableau.

Extract : Extract is an image of the data that will be extracted from the data source and placed into the Tableau repository. This image(snapshot) can be refreshed periodically, fully, or incrementally.

Live : The live connection makes a direct connection to the data source. The data will be fetched straight from tables. So, data is always up to date and consistent. 

48. What are the different joins that Tableau provides?

Joins in Tableau work similarly to the SQL join statement. Below are the types of joins that Tableau supports:

• Left Outer Join
• Right Outer Join
• Full Outer Join

49. What is a Gantt Chart in Tableau?

A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project.

50. Using the Sample Superstore dataset, create a view in Tableau to analyze the sales, profit, and quantity sold across different subcategories of items present under each category.

• Load the Sample - Superstore dataset

• Drag Category and Subcategory columns into Rows, and Sales on to Columns. It will result in a horizontal bar chart.

• Drag Profit on to Colour, and Quantity on to Label. Sort the Sales axis in descending order of the sum of sales within each sub-category.

51. Create a dual-axis chart in Tableau to present Sales and Profit across different years using the Sample Superstore dataset.

• Drag the Order Date field from Dimensions on to Columns, and convert it into continuous Month.

• Drag Sales on to Rows, and Profits to the right corner of the view until you see a light green rectangle.

• Synchronize the right axis by right-clicking on the profit axis.

• Under the Marks card, change SUM(Sales) to Bar and SUM(Profit) to Line and adjust the size.

52. Design a view in Tableau to show State-wise Sales and Profit using the Sample Superstore dataset.

• Drag the Country field on to the view section and expand it to see the States.

• Drag the Sales field on to Size, and Profit on to Colour.

• Increase the size of the bubbles, add a border, and halo color.

From the above map, it is clear that states like Washington, California, and New York have the highest sales and profits. While Texas, Pennsylvania, and Ohio have good amounts of sales but the least profits.

53. What is the difference between Treemaps and Heatmaps in Tableau?

54. using the sample superstore dataset, display the top 5 and bottom 5 customers based on their profit..

• Drag Customer Name field on to Rows, and Profit on to Columns.

• Right-click on the Customer Name column to create a set

• Give a name to the set and select the top tab to choose the top 5 customers by sum(profit)

• Similarly, create a set for the bottom five customers by sum(profit)

• Select both the sets, right-click to create a combined set. Give a name to the set and choose All members in both sets.

• Drag top and bottom customers set on to Filters, and Profit field on to Colour to get the desired result.

Data Analyst Interview Questions On Python

55. what is the correct syntax for reshape() function in numpy .

56. What are the different ways to create a data frame in Pandas?

There are two ways to create a Pandas data frame.

• By initializing a list

• By initializing a dictionary

57. Write the Python code to create an employee’s data frame from the “emp.csv” file and display the head and summary.

To create a DataFrame in Python , you need to import the Pandas library and use the read_csv function to load the .csv file. Give the right location where the file name and its extension follow the dataset.

To display the head of the dataset, use the head() function.

The ‘describe’ method is used to return the summary statistics in Python.

58. How will you select the Department and Age columns from an Employee data frame?

You can use the column names to extract the desired columns.

59. Suppose there is an array, what would you do? 

num = np.array([[1,2,3],[4,5,6],[7,8,9]]). Extract the value 8 using 2D indexing.

Since the value eight is present in the 2nd row of the 1st column, we use the same index positions and pass it to the array.

60. Suppose there is an array that has values [0,1,2,3,4,5,6,7,8,9]. How will you display the following values from the array - [1,3,5,7,9]?

Since we only want the odd number from 0 to 9, you can perform the modulus operation and check if the remainder is equal to 1.

Become a Data Scientist with Hands-on Training!

61. There are two arrays, ‘a’ and ‘b’. Stack the arrays a and b horizontally using the NumPy library in Python.

You can either use the concatenate() or the hstack() function to stack the arrays.

62. How can you add a column to a Pandas Data Frame?

Suppose there is an emp data frame that has information about a few employees. Let’s add an Address column to that data frame.

Declare a list of values that will be converted into an address column.

63. How will you print four random integers between 1 and 15 using NumPy?

To generate Random numbers using NumPy, we use the random.randint() function.

64. From the below DataFrame, how will you find each column's unique values and subset the data for Age<35 and Height>6?

To find the unique values and number of unique elements, use the unique() and nunique() function.

Now, subset the data for Age<35 and Height>6.

65. Plot a sine graph using NumPy and Matplotlib library in Python.

Below is the result sine graph.

66. Using the below Pandas data frame, find the company with the highest average sales. Derive the summary statistics for the sales column and transpose the statistics.

• Group the company column and use the mean function to find the average sales

• Use the describe() function to find the summary statistics

• Apply the transpose() function over the describe() method to transpose the statistics

So, those were the 65+ data analyst interview questions that can help you crack your next data analyst interview and help you become a data analyst. 

Now that you know the different data analyst interview questions that can be asked in an interview, it is easier for you to crack for your coming interviews. Here, you looked at various data analyst interview questions based on the difficulty levels. And we hope this article on data analyst interview questions is useful to you. 

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1) How do I prepare for a data analyst interview? 

To prepare for a data analyst interview, review key concepts like statistics, data analysis methods, SQL, and Excel. Practice with real datasets and data visualization tools. Be ready to discuss your experiences and how you approach problem-solving. Stay updated on industry trends and emerging tools to demonstrate your enthusiasm for the role.

2) What questions are asked in a data analyst interview? 

Data analyst interviews often include questions about handling missing data, challenges faced during previous projects, and data visualization tool proficiency. You might also be asked about analyzing A/B test results, creating data reports, and effectively collaborating with non-technical team members.

3) How to answer “Why should we hire you for data analyst?”

An example to answer this question would be - “When considering me for the data analyst position, you'll find a well-rounded candidate with a strong analytical acumen and technical expertise in SQL, Excel, and Python. My domain knowledge in [industry/sector] allows me to derive valuable insights to support informed business decisions. As a problem-solver and effective communicator, I can convey complex technical findings to non-technical stakeholders, promoting a deeper understanding of data-driven insights. Moreover, I thrive in collaborative environments, working seamlessly within teams to achieve shared objectives. Hiring me would bring a dedicated data analyst who is poised to make a positive impact on your organization."

4) Is there a coding interview for a data analyst? 

Yes, data analyst interviews often include a coding component. You may be asked to demonstrate your coding skills in SQL or Python to manipulate and analyze data effectively. Preparing for coding exercises and practicing data-related challenges will help you succeed in this part of the interview.

5) Is data analyst a stressful job?

The level of stress in a data analyst role can vary depending on factors such as company culture, project workload, and deadlines. While it can be demanding at times, many find the job rewarding as they contribute to data-driven decision-making and problem-solving. Effective time management, organization, and teamwork can help manage stress, fostering a healthier work-life balance.

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About the author.

Shruti is an engineer and a technophile. She works on several trending technologies. Her hobbies include reading, dancing and learning new languages. Currently, she is learning the Japanese language.

Recommended Resources

Data Analyst Resume Guide

Data Analyst vs. Business Analyst: A Comprehensive Exploration

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Business Intelligence Career Guide: Your Complete Guide to Becoming a Business Analyst

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