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Uber Data Science Internship 2025 Preparation Guide — Resources, PYQs, and Interview Questions

Uber Data Science Internship 2025 Preparation Guide — Resources, PYQs, and Interview Questions

Lets Code•October 7, 2025

If you’ve recently received a mail for the Uber Data Science Internship, congratulations! 🎉
Competition is tough, but with the right preparation strategy, you can stand out and crack the interviews.

In this guide, you’ll find:

  • 📘 Complete syllabus breakdown
  • 🧠 Resources to learn Data Science, SQL, Python, and Statistics
  • 🔥 Previous year questions (PYQs)
  • 💡 Project and portfolio ideas
  • 🗂️ Mock interview and resume tips

1. Understanding the Uber Data Science Internship Role

Uber’s Data Science interns work on real-time projects involving:

  • Experimentation and A/B testing
  • Predictive modeling
  • Machine learning for pricing, ETA, supply-demand forecasting
  • SQL-based data analysis and insights

Key Skills Required:

  • Python (Pandas, NumPy, Scikit-learn)
  • Statistics & Probability
  • SQL
  • Data Visualization (Matplotlib, Seaborn, Tableau)
  • Machine Learning concepts (Regression, Classification, Feature Engineering)

2. Preparation Roadmap

Here’s a structured 6-step roadmap to help you prepare efficiently:

Step 1: Brush Up on Python & Data Handling

Step 2: Master Statistics & Probability

Uber’s DS interviews often begin with statistics-heavy questions.

Step 3: SQL for Data Science

You’ll likely get a SQL coding test — similar to LeetCode-style query problems.

Step 4: Machine Learning Fundamentals

You don’t need deep neural networks — just solid ML foundations.

Step 5: Real-World Projects

Projects showcase your practical understanding.

Recommended Project Ideas:

  • Uber Ride Fare Prediction (Kaggle Dataset)
  • Demand Forecasting for Ride Requests
  • Real-time Surge Pricing Simulation
  • A/B Test on Driver Incentive Programs

👉 Host them on GitHub + create a short write-up on Medium/LinkedIn.

Datasets:

Step 6: Mock Interviews & Resume

Uber Data Science Interview Structure

RoundTypeTopics
1Online AssessmentSQL + Statistics MCQs + Python coding
2Technical InterviewML algorithms, case studies, model evaluation
3Business Case / Scenario RoundProblem-solving, A/B testing, hypothesis design
4HR / BehavioralMotivation, teamwork, project ownership

4. Uber Data Science PYQs (Previous Year Questions)

Here are some commonly asked and pattern-based questions from previous Uber Data Science Internship interviews and assessments.
These cover SQL, Statistics, Machine Learning, and Case Studies.

SQL Questions

  1. Find the top 3 drivers by number of completed trips in each city.
  2. Write a query to calculate the percentage of trips that were canceled.
  3. Retrieve users who took more than 3 trips in a single week.
  4. Find the average trip fare per city and order by highest to lowest.
  5. Calculate the total revenue generated per driver in the last 30 days.
  6. Write a query to find the driver with the highest cancellation rate.
  7. Find users who haven’t taken any trips in the last 3 months.
  8. Retrieve the top 5 cities with the highest average rating.
  9. Find the most common pickup location in each city.
  10. Calculate the ratio of completed to canceled trips per driver.
  11. Write a query to find repeat customers (users who took ≥2 trips in a day).
  12. Identify drivers who earned more than the average driver income.
  13. Retrieve total trip distance grouped by vehicle type.
  14. Write a query to get users who gave a 5-star rating in all their trips.
  15. Find drivers whose acceptance rate is below 70%.
  16. Get the count of trips per weekday and weekend.
  17. Calculate average trip duration per hour of the day.
  18. Find the month with the highest number of active drivers.
  19. Write a query to calculate churn — users who haven’t booked in the last 60 days.
  20. Find how many users joined in each month and their first booking date.

Statistics & Probability

  1. How would you design an A/B test to compare two pricing models?
  2. Explain Type I and Type II errors.
  3. What assumptions underlie a linear regression model?
  4. How do you check for multicollinearity?
  5. What is the difference between correlation and causation?
  6. Explain the concept of p-value in hypothesis testing.
  7. How do you calculate confidence intervals for a population mean?
  8. When would you use a t-test vs z-test?
  9. What is the difference between one-tailed and two-tailed tests?
  10. How do you determine the required sample size for an experiment?
  11. Explain the Central Limit Theorem and its importance.
  12. How would you detect outliers in a dataset?
  13. What’s the difference between parametric and non-parametric tests?
  14. Define overfitting in the context of statistical modeling.
  15. Explain variance, bias, and standard deviation in simple terms.
  16. When should you use chi-square test vs ANOVA?
  17. How would you test if two proportions are significantly different?
  18. What is Simpson’s paradox? Give an example.
  19. Explain how missing data can bias results.
  20. How would you check if two features are independent?

Machine Learning

  1. How would you handle imbalanced ride data (e.g., 95% successful, 5% canceled)?
  2. What’s the difference between L1 and L2 regularization?
  3. How do you interpret model coefficients?
  4. Explain feature importance in tree-based models.
  5. What’s the difference between bagging and boosting?
  6. How would you perform feature selection for a large dataset?
  7. Explain ROC curve, AUC, precision, and recall.
  8. How would you detect and handle multicollinearity in regression?
  9. What are hyperparameters, and how do you tune them?
  10. How do you prevent overfitting in a model?
  11. Explain k-fold cross-validation and its purpose.
  12. What’s the difference between classification and regression?
  13. How would you evaluate a model’s performance on imbalanced data?
  14. How do decision trees decide splits?
  15. Explain the working of Random Forest and its advantages.
  16. What is gradient boosting, and how does it differ from AdaBoost?
  17. Explain bias-variance tradeoff in ML.
  18. How would you handle missing values in your dataset?
  19. What’s the difference between supervised and unsupervised learning?
  20. How would you explain your model to a non-technical stakeholder?

Case Study & Scenario-Based Questions

  1. Ride Surge Prediction: How would you predict surge pricing in real-time?
  2. Driver Churn: Uber wants to predict which drivers might stop driving. Design a model for that.
  3. A/B Testing: Uber tests two new features in the app — how will you decide which one performs better?
  4. Cancellation Analysis: Cancellations have increased — how would you identify the reason?
  5. ETA Prediction: How would you build a system to estimate trip time accurately?
  6. Demand Forecasting: Predict the number of rides required in a city based on weather and time.
  7. Pricing Strategy: Uber wants to optimize fare pricing. What data and model would you use?
  8. User Retention: How would you identify users who are likely to churn and suggest retention strategies?
  9. Driver Incentive Optimization: Uber is offering new incentives to drivers. How would you evaluate success?
  10. Customer Segmentation: How would you segment users to target different campaigns effectively?
  11. A/B Test Fail: You ran an experiment but results were inconclusive — what’s next?
  12. Trip Fraud Detection: How would you detect fake or fraudulent trips?
  13. Supply-Demand Gap: During peak hours, demand > supply. How would you model this situation?
  14. Driver Rating Prediction: Predict the next rating a driver will receive.
  15. Operational Efficiency: Suggest a data-driven solution to reduce idle time for drivers.
  16. Feature Impact: How do you measure which feature most affects trip cancellations?
  17. City Expansion: Uber wants to expand to a new city — what metrics would you analyze first?
  18. Data Quality Issue: If you discover inconsistent fare data, how would you handle it?
  19. Feature Deployment: Your model performs well offline but not in production — what steps do you take?
  20. Product Analytics: How would you define success metrics for a new Uber feature (e.g., Uber Reserve)?

💡 Pro Tip:
Uber loves candidates who combine analytical thinking with business understanding. When answering case studies:

  • Define the problem clearly.
  • Specify measurable metrics (retention rate, revenue, satisfaction).
  • Justify your data assumptions.
  • Always link technical insights to business impact.

✅ Next Step:
Once you’ve practiced these PYQs, test yourself on:

5 . Final Tips for Uber Internship Applicants

  • Start early, spend 1–2 hours daily revising SQL and stats.
  • Focus more on problem-solving and interpretation than memorization.
  • Practice explaining models in simple business terms.
  • Build a solid GitHub profile with at least 2 data projects.
  • Stay active on LinkedIn, Uber recruiters often check your activity!

🌟 At Last

Cracking the Uber Data Science Internship is absolutely possible if you follow a smart and consistent approach.
Focus on fundamentals, practice with real datasets, and stay updated with Uber’s analytics challenges.

Good luck, future Uber intern! 💪

Join Telegram group for more resources & discussions!

🧰 Useful Resources for Your Placement Prep

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