data analyst case study interview

Data Analytics Case Study Guide (Updated for 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.

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 .

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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Top 10 Data Science Case Study Interview Questions for 2024

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

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

Source: statists 2021

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

Table of Contents

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

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

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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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Building end-to-end Data Science Workflows — 

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

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

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

Conclude — 

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

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

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

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

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

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

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

Assumptions and Preprocessing—

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

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

End to End Data Science Workflow — 

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

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

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

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

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

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.

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Data Analyst Dream Job: The Ultimate Guide to Interview Preparation

• June 5, 2023

‍If you’re a data analyst looking to land your dream job, then you already know how competitive the market can be. With so many qualified candidates vying for the same position, it can be tough to stand out from the crowd. That’s why it’s essential to prepare thoroughly for your interview, ensuring that you showcase your skills and experience in the best possible light.

In this ultimate guide, we’ll take you through the key steps to interview preparation, from researching the company and the role to practicing your answers to common questions. We’ll also cover the most critical technical skills you’ll need to demonstrate, including data analysis, statistical modeling, and data visualization. With our expert tips and advice, you’ll feel confident and well-prepared when it comes to interview day, increasing your chances of success and landing your dream job as a data analyst. So let’s dive in and get started!

Understanding the Role of a Data Analyst

Before you can prepare for your interview, it’s crucial to understand the role of a data analyst . As a data analyst, your primary responsibility will be to collect, process, and perform statistical analyses on large datasets. You will use this data to identify trends, patterns, and insights that can help inform business decisions and strategies.

To be successful as a data analyst, you’ll need to have a strong foundation in statistics, programming, and data visualization. You should also be comfortable working with large datasets and have experience using tools such as SQL, Python, and R.

In addition to technical skills, data analysts need to have excellent communication and problem-solving skills. You should be able to communicate complex data and insights to non-technical stakeholders in a way that is easy to understand. You should also be comfortable working independently and as part of a team.

Preparation for the Interview

Now that you have a better understanding of the role of a data analyst, it’s time to start preparing for your interview. The first step is to research the company and the role thoroughly. This will help you get a better sense of the company’s culture, values, and mission, as well as the specific requirements of the role you’re applying for.

You should also review the job description carefully, paying close attention to the required qualifications and skills. This will help you tailor your answers to the specific needs of the company and position.

Another important step in interview preparation is to practice your answers to common questions. This will help you feel more comfortable and confident during the interview and ensure that you’re able to showcase your skills and experience effectively.

Common Interview Questions for Data Analysts

During your interview, you can expect to be asked a variety of questions about your experience, skills, and qualifications. Here are some common data analyst interview questions you should be prepared to answer:

• What experience do you have with data analysis?
• What technical skills do you have, and how have you used them in your previous roles?
• How do you approach problem-solving?
• How do you ensure the accuracy and reliability of your data?
• How do you communicate complex data and insights to non-technical stakeholders?

Make sure you have specific examples and stories to illustrate your answers. This will help you stand out from other candidates and demonstrate your skills and experience effectively.

Behavioral Interview Questions for Data Analysts

In addition to technical questions, you may also be asked behavioral questions during your interview. These questions are designed to assess your soft skills, such as communication, teamwork, and problem-solving. Here are some examples of behavioral questions you might be asked:

• Describe a time when you had to work with a difficult team member. How did you handle the situation?
• Tell me about a time when you had to make a difficult decision based on incomplete or conflicting data.
• Describe a time when you had to communicate complex data to a non-technical stakeholder. How did you ensure they understood the information?

Again, make sure you have specific examples and stories to illustrate your answers. This will help you showcase your soft skills and demonstrate how you approach different situations.

Technical Interview Questions for Data Analysts

As a data analyst, you’ll also be expected to demonstrate your technical skills during the interview. This may include questions about statistical modeling, data visualization, and programming. Here are some examples of technical questions you might be asked:

• What statistical models have you used in your previous roles, and how have you applied them to solve business problems?
• What data visualization tools are you familiar with, and how have you used them to communicate insights to stakeholders?
• Can you walk me through your process for cleaning and processing data?

Make sure you’re comfortable with the specific tools and techniques required for the role you’re applying for. You should also be prepared to explain your thought process and decision-making when answering technical questions.

Tips for Answering Interview Questions

Regardless of the type of question you’re asked during the interview, there are some general tips you can follow to help you answer effectively. Here are some tips to keep in mind:

• Be concise and to the point. Avoid rambling or going off on tangents.
• Use specific examples and stories to illustrate your answers.
• Be honest about your strengths and weaknesses.
• Ask clarifying questions if you’re unsure about what the interviewer is asking.

Remember, the goal of the interview is to showcase your skills and experience effectively. By following these tips, you’ll be able to do just that.

Preparing for a Case Study Interview

In addition to traditional interview questions, you may also be asked to complete a case study during the interview. A case study is a simulation of a real-world business problem that you’ll be asked to solve using your data analysis skills.

To prepare for a case study interview, make sure you’re comfortable with the specific tools and techniques required for the role. You should also practice solving case studies on your own to get a better sense of what to expect.

During the case study, make sure you communicate your thought process and decision-making clearly. Don’t be afraid to ask clarifying questions or to take a few minutes to gather your thoughts before answering.

Research the company and position

When preparing for a data analyst interview, it is essential to research the following aspects of the company and the position:

• Company overview : Understand the company’s history, mission, values, products/services, and current market position. This information can be obtained from the company’s website or press releases.
• Industry trends : Research the latest trends, challenges, and opportunities in the industry in which the company operates. This will help you understand the context in which the company operates and how data analysis can contribute to its success.
• Job description : Thoroughly review the job description to gain an understanding of the specific requirements, responsibilities, and qualifications for the position. This will help you tailor your answers to the interviewer’s questions and demonstrate how your skills align with the job requirements.
• Company culture : Research the company’s culture, work environment, and values to determine if it aligns with your own professional goals and values. This information can be obtained from the company’s website, social media, or employee reviews.
• Data tools and technologies : Review the data tools and technologies that the company uses, such as SQL, Python, Tableau, or Excel. If you have experience with any of these tools, highlight it in your resume or during the interview.
• Competitors : Research the company’s top competitors and their data analytics strategies. This will help you understand the competitive landscape and provide insights into how the company can improve its data analysis processes.

Additional resources for interview preparation

• Online courses and certifications: Platforms like Coursera, Udemy, and edX offer various data analyst courses that can help in interview preparation. For example, “Data Analyst Nano-degree” on Udacity covers in-depth knowledge of data analysis, data wrangling, data visualization, and statistics.
• Books : Reading books related to data analysis, statistics, and machine learning can help in understanding the concepts better. Some recommended books include “Storytelling with Data” by Cole Nussbaumer Knaflic, “Data Science for Business” by Foster Provost and Tom Fawcett, and “Python for Data Analysis” by Wes McKinney.
• Online forums and communities : Joining communities like Kaggle, StackOverflow, and Reddit can be helpful in getting answers to interview questions and understanding different perspectives on data analysis problems.
• Practice problems : Practicing data analysis problems can help in increasing the speed and accuracy of solving problems. Websites like LeetCode, HackerRank, and GeeksforGeeks offer practice problems related to data analysis and statistics.
• Mock interviews : Conducting mock interviews with friends or colleagues can help in building confidence and identifying areas of improvement. There are also online platforms like Pramp and Gainlo that provide mock interview services.
• Company-specific resources : Researching the company’s website, annual reports, and job descriptions can help in understanding the company’s culture and requirements. Additionally, looking into company-specific data analysis problems and case studies can provide insights into the company’s data analysis approach.

The Importance of Soft Skills in Data Analysis

While technical skills are crucial for data analysts, soft skills are just as important. Soft skills include communication, teamwork, problem-solving, and time management. These skills are essential for working effectively with others and ensuring that your insights and recommendations are understood and acted upon.

During the interview, make sure you showcase your soft skills as well as your technical skills. Use specific examples to illustrate how you’ve demonstrated these skills in previous roles.

Follow-Up After the Interview

After the interview, it’s essential to follow up with a thank-you email or note. This will help you stand out from other candidates and demonstrate your interest in the role.

In your thank-you note, be sure to thank the interviewer for their time and reiterate your interest in the position. You can also include any additional information or examples that you forgot to mention during the interview.

Preparing for a data analyst interview can be a daunting task, but with the right preparation and practice, you can increase your chances of success. Make sure you research the company and the role thoroughly, practice your answers to common questions, and demonstrate both your technical and soft skills during the interview. By following these tips, you’ll be well on your way to landing your dream job as a data analyst. Good luck!

Some of the frequently asked questions include:

• What are the essential skills required to become a successful data analyst?
• How can you ensure that your resume stands out when applying for a data analyst position?
• What is the best way to prepare for a data analyst job interview?
• How can you showcase your problem-solving abilities during a data analyst interview?
• What are some common challenges faced by data analysts, and how can you overcome them?

You can read more on this on our LinkedIn page as well:

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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.

How The Data Monk can help you?

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• 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
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• 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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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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Data analytics case study data files

Inventory analysis case study data files:.

Beginning Inventory

Purchase Prices

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Ending Inventory

Inventory Analysis Case Study Instructor files:

Instructor guide

Phase 1 - Data Collection and Preparation

Phase 2 - Data Discovery and Visualization

Phase 3 - Introduction to Statistical Analysis

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