23 Take-Home Data Science Challenges (with Examples + Solutions)

Take-home challenges have long been a core component of the data science interview process, particularly outside of big tech. Despite their continued use, take-home challenges are beginning to face significant disruption with the rise of Gen-AI. Candidates are increasingly leveraging these tools, raising concerns about authenticity and originality. As a result, some companies are phasing out traditional take-homes or replacing them with more interactive formats like live case studies or pair programming.

Still, these assignments remain relevant. However, interviewers are now less focused on raw output and more on how you structure the approach and explain trade-offs. In fact, A comprehensive analysis of over 10,000 interview experiences reveals that 25% of data science interviews still include a take-home assignment. For now, take-homes aren’t disappearing—they’re just being redefined.

In this guide, we’ll go through some of the take-home challenges that companies love to repeat, with slight modifications, and present an approach to the solutions:

Analytics take-home challenges are most common in data analyst roles. These challenges commonly provide a dataset and require you to perform exploratory data analysis. In some cases, guiding questions will be asked to help direct your analysis, and often, you’ll be required to make product or business recommendations based on your research.

1. Stripe Analytics Challenge

Overview: Analyze Stripe’s product performance and growth to prioritize product development efforts.

Assignment Details:

• Analyze product usage data for Stripe’s flagship products across different user segments.
• Evaluate the performance and growth trends of each product and segment.
• Identify potential issues and areas for improvement in product offerings.
• Suggest prioritization strategies for product development based on findings.

Deliverables: A short presentation detailing findings, insights, and recommendations.

Approach: Begin by examining the Product Usage Table to identify trends in product usage and revenue generation across different segments. Analyze the Segment Table to understand user behavior and preferences within each segment. Identify key performance indicators (KPIs) such as event counts and USD amounts processed. Highlight any issues or anomalies in product performance. Consider potential areas for deeper analysis, such as user engagement or conversion rates. Finally, recommend prioritization strategies for product development based on insights gained, focusing on maximizing growth and addressing any identified issues. For more insights, explore the Stripe Data Scientist Interview Guide.

2. Airbnb: Rio de Janeiro Booking Growth Takehome

Overview:

The challenge involves analyzing Airbnb’s booking data in Rio de Janeiro to propose strategies for increasing successful bookings.

Assignment Details:

• Identify key metrics to monitor the success of guest-host matching.
• Recommend areas for investment to boost bookings and identify well-performing segments.
• Propose 2-3 business initiatives or product changes with rationale and prioritization.
• Suggest additional research or experiments for better understanding of supply-demand matching.

Deliverables:

A Jupyter Notebook summarizing recommendations for the Head of Product and VP of Operations, with an appendix detailing the analysis.

Approach:

Begin by conducting exploratory data analysis (EDA) on the provided datasets to understand booking patterns and guest-host interactions. Calculate key metrics such as conversion rates, response times, and booking success rates. Use data visualization to identify trends and areas for improvement. Develop recommendations based on insights, focusing on enhancing user experience and optimizing the booking process. Consider additional research methods like A/B testing or user surveys to gain deeper insights into user behavior and preferences. Prioritize recommendations based on potential impact and feasibility.

For more details, visit the Airbnb Take-Home Challenge.

3. Instacart Data Analyst Challenge

Overview: Analyze a dataset to derive insights and propose staffing strategies for a Customer Support Team.

Assignment Details:

• Analyze the provided data.csv file containing order details, locations, customer ratings, and reported issues.
• Identify key observations about the business operations and customer interactions.
• Propose a staffing plan for the Customer Support Team based on your analysis.
• Compile your findings into a document or presentation.

Deliverables: A document or presentation summarizing your analysis and staffing recommendations.

Approach: Begin by loading the dataset into a data analysis tool like Python or R. Conduct exploratory data analysis (EDA) to understand the distribution of orders, customer ratings, and reported issues. Use data visualization to highlight trends and patterns. Identify peak times and locations for customer support needs. Based on these insights, recommend staffing levels and schedules to optimize customer support. Ensure your findings are clearly communicated in a well-structured document or presentation.

4. Masterclass Analytics Assignment

Overview: Analyze user behavior data from the Gordon Ramsay MasterClass marketing page to derive insights and recommendations.

Assignment Details:

• Analyze data from 11/1/2017 to 11/7/2017.
• Data includes page views, clicks, and purchases.
• Files: pages.csv, homepage_click.csv, course_marketing_click.csv, purchase_click.csv, purchased_class.csv.
• Identify user journey and behavior patterns.

Deliverables: Compile findings and insights in a Jupyter Notebook.

Approach: Begin by loading the datasets into a Jupyter Notebook and perform exploratory data analysis (EDA) to understand the structure and content of the data. Use visualizations to map user journeys from landing on the homepage to making a purchase. Identify key metrics such as conversion rates, click-through rates, and user drop-off points. Analyze the impact of different traffic sources and ad types on user behavior. Finally, synthesize insights into actionable recommendations for improving user engagement and conversion rates on the marketing page.

5. Twitter Data Analytics Assignment

Overview: This take-home assignment involves analyzing Twitter’s advertising data to assess the effectiveness of a new ad product in reducing overspending.

Assignment Details:

• Conduct an exploratory data analysis (EDA) on the provided dataset.
• Calculate probabilities for a modified craps game with biased dice.
• Analyze an A/B test to determine the effectiveness of a new ad product.
• Investigate potential budget changes by advertisers in response to the new product.

Deliverables: A comprehensive report with findings, Python or R code, and a clear narrative on methodology and results.

Approach: Start with EDA to understand the dataset’s structure and key metrics. For the craps game, calculate probabilities using the biased dice distribution. In the A/B test analysis, compare overspending rates between control and treatment groups, and assess the impact of company size. Use statistical tests to evaluate the significance of observed differences. Finally, analyze budget data to address concerns about advertisers’ budget adjustments. Ensure all findings are clearly communicated with supporting visuals and well-documented code.

For more details, visit the Twitter Data Scientist Interview Guide.

6. Amazon Take-Home Assignment

Overview: Analyze sales data to estimate the percentage of customers who do not wait for replenishment and instead purchase elsewhere, leading to lost sales.

Assignment Details:

• Analyze sales data to determine customer behavior during stock shortages.
• Assume demand has no trend or seasonality and follows a specific function with noise.
• Customers who return do so only on replenishment days, and each customer buys no more than one item.
• Replenishment occurs at the start of the day.

Deliverables: A model or analysis that estimates lost sales due to shortages.

Approach: Begin by modeling the demand using the given function, incorporating noise to simulate real-world variability. Analyze sales data to identify patterns in customer behavior during stock shortages, focusing on the days leading up to and following replenishment. Use statistical methods to estimate the percentage of customers who do not wait for replenishment. Consider visualizing the data to identify trends and validate assumptions.

7. Capgemini Machine Learning Task

Overview: This take-home challenge involves building and evaluating models to predict national retail store sales, focusing on the seasonal nature of the business.

Assignment Details:

• Use the provided sales_forcasting.csv dataset to predict sales for each store and department.
• Consider the impact of holidays like Super Bowl, Labor Day, Thanksgiving, and Christmas on sales.
• Prepare a 20 to 30-minute presentation demonstrating insights and coding knowledge.
• Ensure the presentation is accessible to a general technical audience.

Deliverables: A Jupyter notebook containing problem description, insights, descriptive statistics, model specifications, diagnostics, and interpretation.

Approach: Begin with exploratory data analysis to understand sales patterns and seasonality. Use time series analysis techniques such as ARIMA or SARIMA to model the data, considering holiday effects. Implement machine learning models like XGBoost or LSTM for comparison. Evaluate models using metrics like RMSE or MAE, and visualize results with charts and graphs. Document the process, assumptions, and insights clearly in the notebook.

8. Capgemini: Spam Article Classifier Takehome

Overview: Develop a machine learning model to classify news articles as spam or valid, and present your findings in a business-friendly format.

Assignment Details:

• Analyze and classify news articles using a machine learning model.
• Use provided datasets: blacklist.json for blacklisted sites and scraped_articles.json for valid articles.
• Prepare a 20-30 minute presentation detailing your analysis, insights, and model specifications.
• Ensure the presentation is accessible to a general technical audience.

Deliverables: A Jupyter notebook containing problem description, insights, descriptive statistics, model specifications, diagnostics, and interpretation.

Approach: Begin with exploratory data analysis to understand the dataset and identify patterns. Use Python libraries like Pandas for data manipulation and Matplotlib or Seaborn for visualization. Preprocess the text data using techniques such as tokenization and TF-IDF vectorization. Choose a classification algorithm like Logistic Regression or Random Forest, and train the model on the dataset. Evaluate the model using metrics like accuracy and F1-score. Finally, interpret the model’s coefficients and present your findings, ensuring to highlight business implications and statistical methodologies. For more details, refer to the Capgemini Spam Article Classifier Takehome.

9. Airbnb Algorithms Take-Home

Overview: Develop a recommendation engine for a rental listing website using user and property data.

Assignment Details:

• Utilize user demographic and interest data alongside property metadata.
• Incorporate topic tags, amenities, price, reviews, and location features.
• Aim to recommend the most relevant rental units to users.
• Consider historical user interactions and search history.

Deliverables: A recommendation engine model with a brief report on methodology and results.

Approach: Start by preprocessing the data to handle any imbalances and missing values. Use collaborative filtering or content-based filtering to create a baseline recommendation model. Enhance the model by incorporating machine learning algorithms like XGBoost or logistic regression to predict the likelihood of a user clicking on a listing. Evaluate the model using metrics such as F1 score and precision/recall, and consider online evaluation through click-through rate (CTR). Explore future improvements by integrating historical user activity data.

10. DoorDash Machine Learning Challenge

Overview:

The DoorDash Delivery Time Estimator take-home challenge involves building a machine learning model to predict delivery times and creating an application to make these predictions.

Assignment Details:

• Build a machine learning model to predict total delivery duration in seconds.
• Write an application to make predictions using the model.
• Use historical delivery data and a JSON file for predictions.
• Evaluate model performance and provide recommendations to reduce delivery time.

Deliverables:

Submit a document explaining your model, code for the model, and a TSV file with predictions.

Approach:

Start by exploring the historical data to understand the features and their relationships. Perform feature engineering to enhance model performance, such as creating time-based features or aggregating data. Choose a suitable regression model, like Random Forest or Gradient Boosting, and evaluate it using metrics like RMSE. For the application, write a Python script that loads the model, processes the JSON input, and outputs predictions to a TSV file. Ensure the code is modular and includes unit tests for reliability.

11. NielsenIQ Machine Learning Take-Home

Overview: Develop a machine learning model to classify text strings as either human-generated or machine-generated.

Assignment Details:

• Two text files are provided: human.txt and machine.txt, each line representing a unique data point.
• The task is to build a model that can classify new text data into one of these two categories.
• You are free to use any combination of techniques and metrics.
• Submit your code and a presentation, with a focus on the justification of methods and metrics.

Deliverables: A zipped folder containing your code and presentation.

Approach: Start by exploring the datasets to understand the characteristics of human vs. machine text. Use Natural Language Processing (NLP) techniques to preprocess the data, such as tokenization, stop-word removal, and vectorization (e.g., TF-IDF or word embeddings). Experiment with different classification algorithms like Logistic Regression, SVM, or Neural Networks. Evaluate models using cross-validation and metrics like accuracy, precision, and recall. Document your process and reasoning in a Jupyter notebook, highlighting key insights and decisions.

12. Uber SQL Take-Home Assignment

Overview: This take-home challenge involves designing metrics for a redesigned Uber Partner app and developing a predictive model to identify new driver signups likely to start driving.

Assignment Details:

• Part 1:
  • Propose a primary success metric for the redesigned app and justify your choice.
  • Identify 2-3 secondary metrics to complement the primary metric.
  • Develop a plan to evaluate the app’s effectiveness using these metrics.
  • Discuss balancing rapid results with statistical validity and risk monitoring.
  • Interpret results to decide on implementing the new design or reverting.
• Part 2:
  • Clean and analyze a dataset of driver signups to determine the percentage that resulted in a first trip.
  • Develop a predictive model to forecast which signups will lead to driving.
  • Discuss model validity and key performance indicators.
  • Suggest strategies for Uber to incentivize more first trips.

Deliverables: A comprehensive report detailing the proposed metrics, evaluation plan, predictive model, and strategic recommendations, including any code used for analysis.

Approach: For Part 1, start by identifying key performance indicators that reflect the app’s success, such as driver engagement or earnings. Use A/B testing to evaluate the app’s impact, ensuring statistical significance while monitoring risks. For Part 2, perform data cleaning and exploratory analysis to understand signup patterns. Develop a predictive model using logistic regression or decision trees, and validate it with metrics like accuracy or AUC. Use insights to recommend strategies for increasing driver engagement, such as targeted incentives or personalized onboarding experiences. For more details, refer to the Uber Data Scientist Interview Guide.

13. NextDoor SQL Coding Take-Home

Overview: Design a KPI dashboard for a fictional company, Photogram, using SQL to analyze user and follower data.

Assignment Details:

• Write SQL queries to explore the dataset: total users, peak user join month, and user with the most followers.
• Plan a KPI dashboard focusing on user growth, photo activity, and follower distribution.
• Design tables to support the dashboard, considering scalability for large datasets.
• Develop SQL queries to update these tables daily.

Deliverables: SQL queries for data exploration, table design for the dashboard, and daily update queries.

Approach: Begin by writing exploratory SQL queries to understand the dataset’s structure and key metrics. Use this understanding to design a scalable table schema that supports the KPI dashboard’s requirements. Focus on efficient data storage and retrieval, considering the potential size of the dataset. Finally, create SQL scripts to automate daily updates, ensuring the dashboard reflects the most current data. This approach balances initial data exploration with strategic planning for long-term data management.

14. McKinsey SQL & EDA Take-Home

Overview: Analyze a bike-sharing dataset to uncover popular routes, station imbalances, and key metrics for program health.

Assignment Details:

• Identify the most popular bike route and the longest route with at least 100 trips.
• Determine the most popular route with an average trip time of at least 8 minutes.
• Analyze station imbalances to find the largest source and sink stations.
• Investigate potential redistribution rides and analyze bike availability at a specific station.

Deliverables: Provide insights on popular routes, station imbalances, and key metrics for program health.

Approach: To tackle this challenge, start by loading the dataset and performing exploratory data analysis to understand the distribution of rides and station usage. Use group-by operations to identify popular routes and calculate average trip times, filtering out trips longer than two hours where necessary. For station imbalances, calculate the net flow of bikes at each station to identify sources and sinks. Investigate redistribution patterns by analyzing time-based bike movements. Finally, propose key metrics such as ride frequency, station balance, and user satisfaction to monitor program health.

15. Qventus Whiteboard SQL Challenge

Overview: This take-home challenge from Qventus involves SQL data manipulation and storytelling to evaluate a machine learning model’s impact on hospital operations.

Assignment Details:

• Part 1: Analyze hospital data using SQL to answer specific questions about patient visits and length of stay.
• Part 2: Develop an analytics story to demonstrate the real-world impact of a machine learning model predicting emergency department surges.
• Time Required: 3 hours for each part.
• Skills Tested: SQL, analytics, data storytelling.

Deliverables: SQL queries for Part 1 and a presentation or document with visualizations for Part 2.

Approach: For Part 1, use SQL to query the provided datasets, focusing on calculating metrics like the percentage of ongoing patient visits and median length of stay by admission class. For Part 2, create a narrative using data visualizations to illustrate the model’s effectiveness, considering factors like prediction accuracy and operational outcomes. Highlight assumptions and potential limitations in your analysis.

16. DraftKings Data Analyst Challenge

Overview: The DraftKings Data Science Challenge Takehome is a three-part assessment designed to evaluate your data analysis, SQL, and programming skills.

Assignment Details:

• Part 1: Interpret a given chart and provide a written explanation of your observations and reasoning.
• Part 2: Use SQL to query a database containing historical bid data to answer specific questions about user activity and player bids.
• Part 3: Write programming functions to solve two tasks: merging two unsorted lists into a sorted union and filtering games based on a specified timeframe.

Deliverables: A document with your chart interpretation, SQL queries, and programming code.

Approach:

1. Chart Interpretation: Analyze the chart by identifying trends, patterns, and anomalies. Consider the context and potential implications of the data presented. Provide a clear and concise explanation of your observations.
2. SQL Queries: Familiarize yourself with the database schema. Write efficient SQL queries to extract the required information, ensuring to optimize for performance where possible. Consider using joins, group by, and aggregate functions to address the questions.
3. Programming Tasks: For the list union task, implement a function that merges and sorts the lists using a language of your choice. For the game filtering task, parse the game data, calculate end times, and filter based on the provided timeframe. Use datetime manipulation to handle time comparisons accurately.

17. Starbucks: Promotion Predictor Takehome

Overview: The Starbucks Promotion Predictor Takehome challenge involves analyzing data to determine which users should receive a promotion to maximize revenue and response rates.

Assignment Details:

• Analyze a dataset of 120,000 data points split into training and test sets.
• Each data point includes features V1-V7, a promotion indicator, and a purchase indicator.
• The goal is to maximize Incremental Response Rate (IRR) and Net Incremental Revenue.
• Focus on sending promotions only to users who would not purchase without it.

Deliverables: A model that predicts which users should receive promotions to optimize IRR and Net Incremental Revenue.

Approach: Begin by performing exploratory data analysis to understand the distribution and relationships of features V1-V7. Use the training data to build a predictive model, such as logistic regression or decision trees, to identify patterns indicating a user’s likelihood to purchase with or without a promotion. Evaluate the model’s performance using the test data, focusing on maximizing IRR and Net Incremental Revenue. Consider using techniques like cross-validation to ensure the model’s robustness. For more insights, visit the Starbucks Data Scientist Interview Guide.

18. Airbnb Growth Take-Home

Overview: This take-home challenge involves analyzing Airbnb’s booking data to propose strategies for increasing successful bookings in Rio de Janeiro.

Assignment Details:

• Identify key metrics to monitor the success of guest-host matching.
• Propose areas for investment to increase successful bookings.
• Provide 2-3 specific recommendations for business initiatives or product changes.
• Suggest additional research or experiments to better understand the supply-demand matching problem.

Deliverables: A Jupyter Notebook summarizing recommendations for the Head of Product and VP of Operations, with an appendix detailing the analysis.

Approach: Start by exploring the provided datasets to understand the booking flow and user interactions. Calculate key metrics such as conversion rates from inquiries to bookings and response times. Use exploratory data analysis (EDA) to identify trends and segments performing well or needing improvement. Develop recommendations based on data insights, prioritizing those with the highest potential impact. Consider additional research methods, such as A/B testing or user surveys, to gain further insights into user behavior and preferences. Present findings in a clear, non-technical narrative suitable for executive stakeholders.

19. Affirm Merchant Analysis Take-Home

Overview: Analyze Affirm’s checkout data to identify data integrity issues, calculate conversion rates, and derive business insights.

Assignment Details:

• Review event data for data integrity issues, ensuring IDs are consistently populated and unique.
• Identify discrepancies between funnel events and completed checkouts.
• Calculate conversion rates through the checkout funnel by day and merchant category.

Deliverables: A Jupyter Notebook with SQL queries, analysis, and business recommendations.

Approach: Begin by loading the datasets into a Jupyter Notebook and perform an exploratory data analysis to understand the structure and content. Use SQL queries to check for data integrity issues such as missing or duplicate IDs. Calculate conversion rates by creating a funnel analysis, segmenting by date and merchant category. Derive insights by analyzing conversion trends and propose recommendations to improve Affirm’s business model. Finally, design an experiment to test one of the recommendations, detailing the hypothesis, structure, success metrics, and potential implementation challenges.

20. Lyft Driver Churn Case Study

Overview: This take-home challenge involves analyzing driver churn for Lyft using data analysis techniques.

Assignment Details:

• Define what constitutes a driver churn and the reasons behind it.
• Calculate the churn rate using provided datasets: driver_ids.csv, ride_ids.csv, and ride_timestamps.csv.
• Cluster drivers into segments based on their churn status.
• Propose strategies to reduce churn and assess their business impact.
• Develop a hypothesis to evaluate the opportunity of reducing churn and its potential effects on the business.

Deliverables: A detailed report addressing each of the above points with supporting data analysis.

Approach: Begin by defining churn using historical ride data to identify patterns indicating a driver has stopped working. Calculate churn rates by analyzing the frequency and recency of rides. Use clustering techniques to segment drivers based on their activity and churn likelihood. Propose strategies such as incentives or improved working conditions to reduce churn, and evaluate their potential impact on business metrics. Formulate a hypothesis to quantify the benefits of reduced churn, using statistical analysis to support your claims.

21. Grubhub Growth Marketing Take-Home

Overview: Analyze Grubhub’s data to determine the best state for product development focus.

Assignment Details:

• Analyze the provided dataset (generated_orders1.csv) containing total orders, visits, and revenue.
• Identify the state with the highest potential for growth and justify your choice.
• Make assumptions where data is insufficient and clearly list them.
• Consider factors like order volume, site visits, and revenue trends.

Deliverables: A report detailing the chosen state, reasoning, and any assumptions made.

Approach: Begin by conducting an exploratory data analysis (EDA) to understand the dataset’s structure and key metrics. Calculate growth potential by comparing order volume, site visits, and revenue across states. Identify trends and patterns that indicate high growth potential. Where data is lacking, make logical assumptions based on industry knowledge and similar market conditions. Conclude with a recommendation for the state that offers the best opportunity for product development focus, supported by data-driven insights.

22. City Year Client Case Study

Overview: Develop a strategic recommendation for City Year on the optimal distribution of corporate versus Red Jacket Society (RJS) development managers.

Assignment Details:

• Analyze the potential engagement and success rates for corporate and RJS development managers.
• Evaluate the financial implications of corporate grants versus individual donations.
• Consider the sustainability and long-term benefits of each funding strategy.
• Identify any additional data that could enhance the recommendation.

Deliverables: A strategic recommendation report suitable for a non-technical executive, including any supporting analysis and scratch work.

Approach: Start by analyzing the financial returns from both corporate and RJS strategies using available data. Compare the sustainability and growth potential of each, focusing on engagement success rates and the longevity of donations or grants. Weigh the initial investment in RJS against the potential for multi-year corporate grants. Identify any data gaps, such as donor retention rates, that could improve the analysis. Craft a balanced recommendation that aligns with City Year’s strategic goals, ensuring it is clear and accessible for non-technical stakeholders.

23. Uber Data Science Challenge

Overview: This take-home challenge involves designing metrics for a redesigned Uber Partner app and developing a predictive model to identify new driver signups likely to start driving.

Assignment Details:

• Part 1: Define a primary success metric for the app redesign, propose secondary metrics, and create an evaluation plan.
• Part 2: Clean and analyze a dataset of driver signups, develop a predictive model, and suggest strategies to increase first trips.

Deliverables: A comprehensive report detailing the metrics, evaluation plan, predictive model, and strategic recommendations, including any code used.

Approach: For Part 1, start by identifying key performance indicators that align with the app’s objectives, such as driver engagement or earnings. Justify your choice of metrics and design an A/B testing framework to evaluate the app’s performance, balancing speed and statistical validity. For Part 2, perform data cleaning and exploratory analysis to understand the dataset. Use machine learning techniques to build a predictive model, such as logistic regression or decision trees, and validate it using metrics like accuracy or AUC. Finally, interpret the model’s insights to propose strategies for increasing driver engagement.

In this Interview Query video, Jay provides an overview of how to pass data science take-home challenges. Specifically, the video offers tips for approaching a take-home, what you should include in your submission, and questions you should ask before you get started. See his data science take-home advice here:

To succeed in take-home challenges, it’s essential to start by clarifying expectations. Ask the recruiter upfront about the timeline, how submissions will be evaluated, and whether you’ll receive feedback. Throughout the challenge, clearly state your assumptions—being transparent about any constraints or decisions helps reviewers understand your thought process.

Follow modeling best practices: clean the data thoroughly, select features thoughtfully, handle missing values appropriately, and ensure your models are well-tuned and organized within a structured pipeline. Present your work professionally by using a consistent project structure, such as the Cookiecutter framework.

Make sure your code is clean and well-documented. Include comments where necessary and provide basic tests to demonstrate quality and clarity. Finally, keep your summary brief and focused—most reviewers spend less than 10 minutes on each submission, so make your key points easy to find and understand.