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Nailing An Analytics Interview Case Study: 10 Practical Strategies

By gabriel zhang.

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.

case study interview data analyst

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.

case study interview data analyst

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.

case study interview data analyst

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.

case study interview data analyst

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?

case study interview data analyst

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.

case study interview data analyst

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.

case study interview data analyst

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.

case study interview data analyst

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.

case study interview data analyst

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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Case Study Interview Questions for Analytics – Day 5

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

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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We have created products and services on different platforms to help you in your Analytics journey irrespective of whether you want to switch to a new job or want to move into Analytics. Our services

  • YouTube channel  covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions Link –   The Data Monk Youtube Channel
  • Website –  ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study Link –   The Data Monk website
  • E-book shop –  We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions Link – The Data E-shop Page
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We are a group of 30+ people with ~8 years of Analytics experience in product-based companies. We take interviews on a daily basis for our organization and we very well know what is asked in the interviews. Other skill enhancer website charge 2lakh+ GST for courses ranging from 10 to 15 months. We only focus on making you a clear interview with ease. We have released our Become a Full Stack Analytics Professional for anyone in 2nd year of graduation to 8-10 YOE. This book contains 23 topics and each topic is divided into 50/100/200/250 questions and answers. Pick the book and read it thrice, learn it, and appear in the interview. We also have a complete Analytics interview package – 2200 questions ebook (Rs.1999) + 23 ebook bundle for Data Science and Analyst role (Rs.1999) – 4 one-hour mock interviews, every Saturday (top mate – Rs.1000 per interview) – 4 career guidance sessions, 30 mins each on every Sunday (top mate – Rs.500 per session) – Resume review and improvement (Top mate – Rs.500 per review)

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Author: TheDataMonk

I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :) View all posts by TheDataMonk

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Data science case study interview

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

TABLE OF CONTENTS

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

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

I   What to expect in the data science case study interview

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

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

II   Recommended framework

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

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

III   Interview tips

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

Articulate your thoughts in a compelling narrative.

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

Example 1: Your interviewer will notice if you say “correlation matrix” when you actually meant “covariance matrix”.
Example 2: Mispronouncing a widely used technical word or acronym such as Poisson, ICA, or AUC can affect your credibility. For instance, ICA is pronounced aɪ-siː-eɪ (i.e., “I see A”) rather than “Ika”.
Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Tie your task to the business logic.

Example 1: If you are asked to improve Instagram’s news feed, identify what’s the goal of the product. Is it to have users spend more time on the app, users click on more ads, or drive interactions between users?
Example 2: You present graphs to show the number of salesperson needed in a retail store at a given time. It is a good idea to also discuss the savings your insight can lead to.

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

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

Brush up your data science foundations before the interview.

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

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

Avoid clear-cut statements.

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

Study topics relevant to the company.

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

Example 1: If the team is working on time series forecasting, you can expect questions about ARIMA, and follow-ups on how to test whether a coefficient of your model should be zero.
Example 2: If the team is building a recommender system, you might want to read about the types of recommender systems such as collaborative filtering or content-based recommendation. You may also learn about evaluation metrics for recommender systems ( Shani and Gunawardana, 2017 ).

Listen to the hints given by your interviewer.

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

Show your motivation.

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

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

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

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

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

IV   Resources

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

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

case study interview data analyst

  • 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".

case study interview data analyst

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case study interview data analyst

Data Science Case Study Interview: Your Guide to Success

by Enterprise DNA Experts | 10:29 pm EST | November 28, 2023 | Careers

Data Science Case Study Interview: Your Guide to Success

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!

Data Science Case Study Interview

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

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.

Problem-Solving Scenarios in Data Science Interview

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.

Evaluation Metrics and Validation for case study interview

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

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

Tips to Master Data Science Case Study Interviews

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

Communication and Presentation of Results in interview

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

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

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.

case study interview data analyst

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

Case interview examples - 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 extract (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)
  • 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

Interview coach and candidate conduct a video call

How to Prepare for a Data Analyst Interview

How to Prepare for a Data Analyst Interview

Preparing for a data analyst interview can seem like a mystery, and we regularly hear from members about how to study and what to expect.

One of the best tips is this: Work backwards when you create a study plan for your analyst interview. Here’s how:

1. Research the interview format by asking the recruiter for direction or searching for interview experiences and interview guides.

2. Mine the job description for the exact skills the company is looking for, usually a mix of SQL, business sense, analytics, and visualization.

3. Build your business sense by researching the company. Think about the business model, who the customers are, and the KPIs you would use to monitor the health of the company.

4. Study and practice real interview questions. For SQL, you’d want to practice easy, medium and hard questions, and for business sense, you’d want to focus on business and analytics case studies.

5. Conduct some mock interviews to simulate the process. It’s difficult to do by yourself. Working with a peer, colleague or coach will help you determine where you struggle and what needs work.

Ideally, you have about a month to prepare. This will allow you to assess the gaps in your knowledge, develop and practice core concepts, and build your interviewing skills. But even if you have less time, there’s plenty you can do.

This interview prep guide offers direction, covering what to study, how to study, and what you can do to prepare for a data analyst interview.

Analyst Interview Prep: Where to Start?

It’s difficult to study for a test, if you don’t know the format. Fortunately, there are many resources available to you that will offer insights into the process, and help you know exactly what to expect.

Here are interview prep tips and resources you can use:

  • Ask the Recruiter - Don’t hesitate to ask the recruiter for some direction. They’ll likely give you an overview of what to expect, the types of questions that will be asked, and how the interview will be conducted.
  • Interview Experiences - Read first-hand accounts of what the interview was like. There are tons of great sources like Blind or Interview Query’s interview experiences discussion posts . You can get some great insights into the questions that were asked, as well as process.
  • Interview Guides - We’ve published interview guides for several FAANG companies. These are based on interview experience posts, as well as compiling data collected from more than 10,000 interviews and job postings.
  • Amazon data analyst interview guide
  • Facebook data analyst interview guide
  • Google data analyst interview guide

Data Analyst Interview Prep: Building Business Sense

As a data analyst, your job will be to solve problems for businesses. But what types of problems does the business need you to solve?

This is a question you have to answer before you even begin prepping. Having a strong grasp of the business model, customers and potential problems they may face will help you narrow your study prep.

In particular, you should be thinking about:

  • The company’s business model and what drives revenue
  • The most important metrics to track for the business
  • The company’s customers and products
  • How customers are acquired
  • The types of data problems the company might face

Why This Matters Data analyst roles require technical and business savvy. Therefore, you need to think about data from a business perspective, and understand which data insights can help move the business forward.

Answering these questions will also help you direct your attention to the types of questions to study. You can look for analytics case study questions that are similar to the types of problems you’ll be facing, as well as SQL questions that ask you to write queries that pull the types of metrics you expect to pull.

What Questions to Study for Data Analyst Interviews

The next phase of the data analyst interview is plotting out what to study and how to prioritize your study. Although your focus will vary by the analyst role you’re preparing for, there are three areas you’ll need to focus your attention. They are:

1. Technical Skills

SQL is a must-have skill for analyst roles, and fortunately, practicing SQL is straightforward. You start with easy and medium difficulty problems, and work your way up to advanced problems.

During the interview, your goal is to write clean code, as efficiently as possible. One good way to practice this is benchmarking your progress. Time how long it takes to complete a problem, and see if you can continue to improve your speed in questions at that level. Most 30-minute technical screens for data analyst roles include 1-2 medium-level SQL questions, and therefore, that should be a goal in your prep.

Beyond SQL, the job description will offer clues about other technical subjects to study. Some of the most common include:

  • Data visualization tools
  • Microsoft Excel
  • Python or R
  • A/B testing and experimentation

Questions to study: Practice easy, medium and hard SQL data analyst problems . Familiarize yourself with core concepts in Excel and Tableau, and a basic understanding of Python or statistical coding can be helpful.

2. Business Sense

Strong SQL skills will only get you so far; you also need to have solid business intuition. And that’s a more difficult skill to study for.

In particular, you should know:

  • The most important KPIs to measure business health
  • Choosing the right metrics for a business problem

Developing business sense starts with understanding the company. If you know the business model, understand how the company can make or lose money, and understand how the business acquires customers, defining metrics becomes easier.

Questions to study: Focus on business and analytics case questions . These questions present a business or analytics problem, and you’ll be responsible for determining how to diagnose the problem, the metrics you will track, and how you would go about the work.

3. Data Intuition

Data intuition can be defined as your ability to read numbers and make sense of them quickly. An analyst with good data intuition understands when a conversion rate is low, or if the numbers look off. In other words, they know where to look in the data to find a solution.

Like business sense, you can develop this skill by practicing analytics case and product metrics case questions. This will help you practice diving into data problems and communicating how you would approach the problem.

Additionally, it’s helpful to brush up on statistics and probability concepts. For example, you should be able to talk confidently about causality vs correlation, P-values, confidence intervals, etc.

Questions to study: Practice general data analyst interview questions , as well as hypotheticals and analytics case studies. Statistics and probability questions are also helpful, both definitions and logic-based questions.

Finally, many behavioral questions for data analysts also ask you about past projects, data problems you’ve solved, etc. and these are also great for building this skill.

How to Study for Data Analyst Interviews

Everyone’s study plan is different, but here’s one tip for structuring your study time. Prioritize your study by the most important skills for the data analyst role, followed closely by where you have gaps in your knowledge.

Here’s how you can do that:

  • Get a baseline. Find real interview questions in each of the categories you’ve identified. Then, run through them and see what comes easy and what you’re struggling with. Prioritize subjects you struggle with most by importance to the role.
  • Create a roadmap for your prep. Plot out the time you have to study before your interview and schedule blocks for different skills based on your priorities from Step 1.
  • Practice, practice, practice. The bulk of your study will be working on practice problems. Start with easy problems, and work up to medium and hard problems. ****
  • Schedule mock interviews. Mock interviews help you get more comfortable with interviewing. Ideally, you’d schedule 2-3 mock interviews before the actual interview.

As you study, don’t forget to benchmark your progress. Conducting mock interviews or working with a data science coach is one of the best ways to do this.

At a minimum, you should be tracking the questions you’ve answered, the difficulty of the question, time to solve (for longer-form questions), and how easy it was for you to answer..

How Long Do You Need to Prepare?

Ideally, you would have a month to study for a data analyst interview. That would give you  enough time to research the company and role, plot out your study plan, practice and do some mock interviews.

In fact, we recommend that you start studying before you even apply for jobs. But even if you only have a few weeks, or just a weekend, the same rules apply. Focus on core skills for the role and gaps in your knowledge, practice as much as possible, and try to fit in at least one mock interview, even if it’s just a peer-to-peer interview.

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