How to Solve Case Studies | Case Studies - Interview Preparation PDF Download

Introduction

When it comes to data science interviews, case studies can be the most difficult part. These scenarios are designed to imitate real projects a company may have undertaken, testing a candidate's ability to analyze information, communicate their insights, and overcome obstacles.

To succeed in data science case study interviews, it's essential to practice. This will help you develop approaches for tackling case studies, asking your interviewer relevant questions, and providing answers that demonstrate your abilities while working within time constraints.

One effective method is to use a framework for answering case studies. For instance, the product metrics framework and A/B testing framework are useful for answering the majority of case studies that may arise in data science interviews.

There are four main types of data science case studies:

1. Product Case Studies: These types of case studies revolve around a particular product or feature of a company that is conducting the interview. The interviewers usually evaluate the candidate's business sense, specifically in terms of product metrics.
2. Data Analytics Case Study Questions: Data analytics case studies require the candidate to propose metrics for analyzing an analytics problem. The candidate is also required to write a SQL query for pulling the proposed metrics and perform data analysis, similar to what they would do in an actual data science role.
3. Modeling and Machine Learning Case Studies: Modeling case studies are diverse and focus on assessing the candidate's ability to build models for solving business problems.
4. Business Case Questions: These types of case studies are similar to product case studies but instead address issues or opportunities specific to the interviewing company. The candidate is generally asked to evaluate the best approach to solve a particular business problem and formulate a process for doing so.

How Case Study Interviews Are Conducted

When it comes to case study interviews, it is essential to understand the setting and format in which the questions will be asked. However, the process can vary depending on the company conducting the interview.

Some companies prefer a real-time setting where candidates work through a prompt immediately, while others may offer a period of several days before asking the candidate to present their findings.

To prepare for any possible circumstance, it's crucial to have a system for answering these questions that can adapt to different formats. A framework can be helpful in this regard, ensuring that you are prepared for any situation that may arise during the interview.

Why Are Case Study Questions Asked?

Case study questions are a way for interviewers to assess your problem-solving skills in the context of data science. They want to see how you think and work through real-world problems that may not have a clear right or wrong answer. This is important because in a real-world business setting, decisions are rarely binary and require careful consideration of various factors.

Additionally, case study questions evaluate your ability to communicate effectively. Data scientists often work with different teams and departments, so it's crucial that you can explain your thought process and conclusions clearly and efficiently.

Overall, case study questions are an important aspect of the interview process as they help employers gauge your ability to handle ambiguity and communicate effectively.

How to Answer Data Science Case Study Questions

The process of handling case questions in Data Science interviews can be broken down into four main steps: clarifying, making assumptions, gathering context, and providing data points and analysis. Regardless of the type of case question, these steps can be applied.

Step 1: Clarify
Clarifying is important because case studies can often be vague and difficult to understand. The candidate must ask questions to gather more information, filter out bad information, and fill gaps. This step involves asking questions such as what the product is, how it works, and how it aligns with the business.

For instance, when dealing with a product question, some important questions to ask include:

What is the product?
How does it work?
How does it align with the business?

Step 2: Make Assumptions
Once the candidate understands the dataset, they can start investigating and discarding possible hypotheses. Developing insights on the product at this stage complements their ability to glean information from the dataset. The candidate should communicate their hypotheses with the interviewer, who can provide clarifying remarks on how the business views the product and help discard unworkable lines of inquiry. Evaluating and drawing conclusions from questions like who uses the product and what the goals of the product are can help reduce the scope of the problem.

Step 3: Propose a Solution
After forming a hypothesis that has incorporated the dataset and an understanding of the business-related context, the candidate can use that knowledge to propose a solution. The hypothesis is a refined version of the problem that uses the data on hand as its basis for being solved. The solution can target this narrow problem and address the core of the case study question. The candidate should keep in mind that there isn’t a single expected solution, and there is some freedom to determine the exact path for investigation.

Step 4: Provide Data Points and Analysis
Finally, providing data points and analysis in support of the solution involves choosing and prioritizing a main metric that ties back to the hypothesis and the main goal of the problem. It is important to trace through and analyze different examples from the main metric in order to validate the hypothesis.

Role of Effective Communication

Effective communication plays a crucial role in the success of case studies in Data Science interviews. According to various articles and discussions by interviewers, the ability to effectively communicate one's thought process and problem-solving skills is a key factor in impressing the interviewers. It is not enough to analyze the data; interviewees must also be able to articulate their thought process and findings verbally. Interviewers look for well-developed "soft-skills" in candidates and their ability to communicate effectively. To improve communication skills, candidates can practice going through example case studies with a friend in an interview-like setting with no access to external resources. While this may be uncomfortable at first, it can help reveal weaknesses and improve investigation skills. Through practice, candidates can gain self-confidence and improve their ability to assess and learn from these sessions.

Product Case Study Questions

When it comes to product data science case questions, the interviewer aims to gauge your product sense intuition. More specifically, these questions evaluate your capacity to determine which metrics are necessary to comprehend a product.

Q.1. How can the success of Instagram's private stories, which are visible only to selected close friends, be measured?

To assess the success of the private stories feature on Instagram, the objective of the product needs to be determined first. Identifying the initial purpose of the feature is crucial before evaluating its success.

One of the goals of the feature is to enhance user engagement. It could potentially increase interactions between users and awareness of the feature.

To evaluate user engagement, several metrics can be proposed, such as the average number of stories per user per day and the average number of Close Friends stories per user per day. However, it would be beneficial to further segment users based on demographics or other metrics to determine the impact of Close Friends stories on user engagement. This approach provides insights on the success of the feature for specific populations, which may not be evident by examining the overall user base.

Q.2. How would you measure the success of acquiring new users through a 30-day free trial at Netflix?

The case study focuses on measuring the success of acquiring new users through a 30-day free trial at Netflix. The company is running a promotion where users can avail of a 30-day free trial and will be charged automatically based on their selected package after the trial period ends. The challenge is to determine the acquisition success and suggest metrics for measuring the success of the free trial.

The first step is to identify controllable inputs, external drivers, and the observable output. The major goals of Netflix are acquiring new users and increasing retention while decreasing churn. To measure acquisition output metrics, several top-level stats can be examined, including conversion rate percentage, cost per free trial acquisition, and daily conversion rate.

However, it is crucial to segment users by cohort to analyze the percentage of free users acquired and retention by cohort. This approach will help determine the success of the free trial in attracting new users and retaining them beyond the trial period.

In conclusion, measuring the success of acquiring new users through a 30-day free trial at Netflix requires examining controllable inputs, external drivers, and the observable output. Conversion metrics and cohort analysis are essential in determining the success of the free trial in achieving the company's acquisition and retention goals.

Q.3. How would you measure the success of Facebook Groups?

To measure the success of Facebook Groups, we need to start by understanding their key function, which is to allow users to connect with others who share similar interests or real-life relationships. This helps users experience a sense of community, which in turn drives our business goal of increasing user engagement.

One way to measure the success of Facebook Groups is to monitor objective metrics like the number of Groups monthly active users. This would help us see if the user base is increasing or decreasing. Additionally, we could track metrics such as posting, commenting, and sharing rates to see if users are actively engaging with the content in these Groups.

However, it's important to also consider the impact of Groups on other Facebook products, specifically the Newsfeed. We need to evaluate if updates from Groups clog up the content pipeline and if users prioritize those updates over other Newsfeed items. This evaluation will give us a better understanding of whether Groups actually contribute to higher engagement levels or not. By analyzing both the direct and indirect impact of Groups on user engagement, we can accurately measure the success of this feature on Facebook.

4. How would you diagnose why weekly active users are up 5%, but email notification open rates are down 2%?

What assumptions can you make about the relationship between weekly active users and email open rates? With a case question like this, you would want to first answer that line of inquiry before proceeding.

The given problem involves diagnosing why weekly active users are up 5%, but email notification open rates are down 2%. To begin with, we need to understand the relationship between weekly active users and email open rates. It is possible that there is no relationship between the two metrics, and they could be influenced by different factors.

We can assume that email open rates are calculated based on the number of people who received the email and the number of people who opened it. Therefore, the open rate can decrease if the numerator (number of people who opened the email) decreases or the denominator (number of emails sent) increases.

With these assumptions, we can come up with some hypotheses for the decrease in open rates and the increase in weekly active users. For instance, it is possible that the increase in weekly active users resulted in more emails being sent overall, which could have led to a decrease in the open rate. Alternatively, it could be that the decrease in open rate is due to a change in email content or design, which is not resonating well with the users.

In order to diagnose the problem, we could perform a more detailed analysis by breaking down email open rates by various user segments and looking at trends over time. We could also conduct A/B tests with different email content or design to determine if that is the root cause of the decrease in open rates.

Data Analytics Case Study Questions

Q.1. After implementing a notification change, the total number of unsubscribes increases. Write a SQL query to show how unsubscribes are affecting login rates over time.

To tackle this problem, we need to first understand the context and the data we have available. Let’s assume we have access to two tables: events (which includes login, nologin, and unsubscribe) and variants (which includes control or variant). We also know that the new notification system has caused an increase in unsubscribes.

To show how unsubscribes are affecting login rates over time, we need to write a SQL query that uses GROUP BY to group data by date and variant. We can then calculate the login rates for each bucket of the A/B test and compare them to the number of unsubscribes.

Some hypotheses that we can explore with this data include:
(i) Whether the notification change affected the overall login rates
(ii) Whether there is a correlation between the number of unsubscribes and login rates
(iii) Whether there is a significant difference between the control and variant groups in terms of login rates and unsubscribes.

By analyzing these variables over time, we can gain insight into how the notification change impacted user behavior and take steps to optimize the system accordingly.

Q.2. How can we disprove the hypothesis that data scientists who switch jobs more often end up getting promoted faster using a provided table of user experiences representing each person's past work experiences and timelines?

To disprove this hypothesis, we need to analyze the dataset and look for patterns that contradict the hypothesis. One approach could be to group data scientists by the number of job switches they have had and compare their promotion rates.

For example, we could find that data scientists who have never switched jobs have a higher promotion rate compared to those who have switched jobs multiple times. Alternatively, we could find that data scientists who have switched jobs frequently have a lower promotion rate compared to those who have stayed with the same company for a long time.

By examining the promotion rates of data scientists with different job switching histories, we can disprove the hypothesis that data scientists who switch jobs more often end up getting promoted faster. We can write a SQL query to group the data by job switching history and analyze the promotion rates of each group.

Q.3. Given a table with search results on Facebook, how would you investigate the hypothesis that click-through rate (CTR) is dependent on search result rating?

Assuming that the table includes columns such as query (search term), position (search position), and rating (human rating from 1 to 5), with each row representing a single search and including a column has_clicked that represents whether a user clicked or not.

To investigate this hypothesis, we need to first create a metric that can analyze the problem and then compute that metric. In this case, our output metric is CTR (clickthrough rate).

We can start by calculating the overall CTR and then looking at how CTR varies across different search result ratings. For instance, we can group the results by rating and calculate the average CTR for each group.

If we find that CTR is higher when search result ratings are high and lower when they are low, then we can conclude that the hypothesis is true. Conversely, if CTR is low when search result ratings are high or there is no correlation between the two, then the hypothesis is not supported.

We can also look at how the relationship between search result ratings and CTR varies by other factors, such as the position of the search result or the search query. This can help us gain a more nuanced understanding of the relationship between these variables.

Modeling and Machine Learning Case Questions

Modeling and Machine Learning Case Questions evaluate a candidate's ability to build models and use machine learning to solve business problems. The questions can cover a wide range of topics, from applying machine learning to a specific case scenario to evaluating the validity of an existing model. The case study requires the candidate to analyze and explain different parts of the model building process.

Q.1. How would you evaluate the predictions of an Uber ETA model?

To evaluate the predictions of an Uber ETA model, we can start by comparing the predicted ETAs to the actual ETAs. We can do this by computing evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. These metrics will help us assess how well the model is performing and whether it is making accurate predictions.

In addition to these metrics, we can also visualize the predicted ETAs against the actual ETAs using scatter plots or histograms. This will help us identify any patterns or trends in the data that the model may be missing. We can also use techniques such as residual analysis to identify any patterns in the errors that the model is making.

Finally, we can perform A/B testing to evaluate the performance of the model in a real-world setting. This will involve randomly assigning riders to either the new ETA prediction model or the existing model and comparing the performance of the two models. If the new model performs better than the existing model, we can roll it out to all riders.

In summary, evaluating the performance of an Uber ETA model involves using evaluation metrics, data visualization, residual analysis, and A/B testing.

Q.2. What are the considerations to be taken into account when building a model that sends bank customers a text message when fraudulent transactions are detected?

This problem is a classic example of a binary classification problem. In this case, we need to identify if the transaction is fraudulent or not. To build this model, we need to consider several factors, such as:

Data collection: The bank must have a large and diverse dataset of transactions that includes both fraudulent and legitimate transactions to train the model. The data should be comprehensive, accurate, and up-to-date to ensure that the model can identify new and emerging types of fraud.

Feature engineering: Once we have collected the data, we need to identify which features are relevant for predicting fraud. Features such as transaction amount, transaction location, and time of the day can be useful for predicting fraud.

Model selection: We need to select an appropriate machine learning model that can classify transactions as fraudulent or legitimate. A logistic regression, decision tree, or random forest algorithm could be suitable for this problem.

Model evaluation: After selecting the model, we need to evaluate its performance on the test dataset. The evaluation metrics can include accuracy, precision, recall, F1 score, and ROC AUC score.

Text message notification: Once the model identifies a fraudulent transaction, the bank should send a text message to the customer informing them of the suspicious activity. Additionally, the customer should be able to approve or deny the transaction via text response. The bank must have a mechanism in place to ensure that customer responses are authenticated to prevent further fraud.

Q.3. How would you provide a rejection reason to each loan application that is rejected using a binary classification model that pre-approves candidates for a loan, given that you do not have access to the feature weights?

To solve this problem, we need to first understand the problem scenario. We are given a binary classification model that pre-approves candidates for a loan, and our goal is to provide a rejection reason to each loan application that is rejected. Since we do not have access to the feature weights, we need to think about how we can reason out the model's decision.

We can start by simplifying the model and assuming that the only features are the total number of credit cards, the dollar amount of current debt, and credit age. We can then take a small sample size of loan applicants, such as Alice, Bob, and Candace, and analyze their loan applications.

For example, if Candace is approved for a loan and Bob and Alice are rejected, we can reason that Candace's $10K in debt swung the model to approve her for a loan. We can then use this reasoning to analyze a larger sample size of loan applicants with similar credit cards, credit age, and varying levels of debt. We can plot the model's average loan acceptance rate for each numerical amount of current debt on a graph to model the y-value (average loan acceptance) versus the x-value (dollar amount of current debt). These graphs are called partial dependence plots, and they can help us reason out the model's decision for each rejected loan application.

Business Case Questions

During data science interviews, business case study questions are used to test your ability to address business-related problems. These questions might cover topics such as estimation, calculation, and problem-solving for larger case scenarios. To prepare, it is important to research the company's products and ventures before the interview to familiarize yourself with potential topics.

Q.1. How would you estimate the average lifetime value of customers at a business that has existed for just over one year?

When presented with a business case study question such as this, it is important to approach it systematically. To estimate the average lifetime value of customers for a new business, we need to consider a few factors.

First, we know that the product costs $100 per month and has a 10% monthly churn rate. This means that every month, 10% of the customers cancel their subscription. Therefore, the average customer stays with the business for 3.5 months.

However, this estimate may be biased as the business has only existed for just over a year. We need to consider the possibility that some customers may have left earlier or later, which could affect the lifetime value. One approach to address this bias is to use a survival analysis model that can estimate the probability of a customer leaving at any given time.

Another consideration is the cost of acquiring new customers. If the cost is high, it could impact the profitability of the business and the lifetime value of each customer. Therefore, it is important to factor in the customer acquisition cost (CAC) to get a more accurate estimate of the lifetime value.

In summary, to estimate the average lifetime value of customers for a new business, we need to consider the product cost, monthly churn rate, length of time customers stay, survival analysis, and customer acquisition cost.

Q.2. What metrics would you monitor to know if a 50% discount promotion is a good idea for a ride-sharing company?

When presented with a business case study problem like this, it's important to break it down into individual components to answer it thoroughly. In this case, we can identify the following:

Problem: What metrics would you monitor to know if a 50% discount promotion is a good idea for a ride-sharing company?

Assumptions: It is assumed that the goal of the discount is to grow revenue and increase retention, and that the promotion will be applied uniformly across all users, and that the 50% discount can only be used for a single ride.

Metrics to monitor:
(i) Long-term revenue: The goal of the promotion is to increase revenue, so we should monitor the long-term impact of the promotion on revenue. It would be important to track any changes in revenue over time after the promotion is launched to ensure that the promotion is having a positive effect on revenue.
(ii) Average cost of the promotion: To determine whether the promotion is profitable, it's important to track the average cost of the promotion. This will include costs associated with implementing the promotion, such as marketing expenses and any discounts offered to customers.
(iii) Evaluation: To evaluate whether the promotion is a good idea, we would need to compare the long-term revenue generated by the promotion to the average cost of the promotion. If the long-term revenue exceeds the cost of the promotion, then the promotion would be considered a good idea.

Q.3. How would you determine the next partner card for a bank, considering the business reasons for credit card partnerships and access to all customer spending data?

To start, it's important to understand why credit card partnerships are valuable for a bank - they help increase customer acquisition and retention. With access to all customer spending data, one approach would be to sum all transactions grouped by merchants to identify the ones that have the highest spending amounts. However, a high-spend value doesn't necessarily mean high volume, so it's important to consider both factors when evaluating potential partners.

Additionally, it's important to consider other factors such as the bank's target audience and their spending habits, as well as the potential benefits that a partnership could provide (e.g. rewards, discounts). By asking questions and considering all relevant factors, a bank could make an informed decision on the best partner for their next credit card.