Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
HardSystem Design
0 practiced
Design a streaming solution to compute Daily Active Users (DAU) from event streams that may contain duplicate event emissions and out-of-order arrivals. Requirements: accurate per-user-per-day deduplication, support 15-minute incremental updates, scale to 200M events/day, and reconcile with a daily batch job. Describe algorithms, state management, watermarks, and storage choices.
EasyTechnical
0 practiced
List common strategies to handle missing data when preparing features for a machine learning model. For each strategy (drop, mean/mode imputation, forward/backward fill, model-based imputation, missingness indicator), explain when it is appropriate, trade-offs, and how it can bias downstream model performance.
EasyTechnical
0 practiced
Explain what the Pearson correlation coefficient measures and why a strong correlation between two variables does not imply a causal relationship. Provide a product analytics example where correlation could be misleading and what analysis you would run to investigate causality.
HardTechnical
0 practiced
You only have observational data but need to estimate the causal effect of a new feature on retention. Compare difference-in-differences (DiD), propensity score matching (PSM), and instrumental variables (IV) methods. For each: explain key assumptions, when it is appropriate, steps to implement, and how you'd validate the results.
EasyTechnical
0 practiced
Coding (Python/pandas): Write a function that takes a pandas DataFrame df with columns ['user_id', 'event', 'value', 'timestamp'] and returns a DataFrame grouped by user_id with columns ['user_id', 'avg_value', 'event_count']. Ignore rows where value is NaN when computing the mean and ensure the result has integer event_count.
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