This was the core of the whole thing and it took me a while to even parse what they wanted.
Start by clarifying the definitions and assumptions, then outline a step-by-step plan: join events and users, filter bots, identify accidental clicks using dwell time and back navigation, aggregate daily per-banner impressions and valid clicks, and compute CTR. Emphasize the importance of handling edge cases like multiple back navigations and time windows.
Pro tip: When defining accidental clicks, consider that a back navigation within 2 seconds might occur after the dwell time threshold; ensure you capture both conditions without double-counting. Also, discuss how to handle users with multiple sessions and the potential need for sessionization.
Confirm what constitutes an impression, a click, and a back navigation event. Clarify if 'dwell under 500ms' applies only to clicks and if back navigation is a separate event type.
Join events and users DataFrames on user_id. Filter out bot users based on a bot flag or heuristic (e.g., user_agent, activity patterns).
For each click event, check if dwell time < 500ms. Also, check if there is a back navigation event from the same user within 2 seconds after the click. Mark clicks meeting either condition as accidental.
Filter events to last 7 days. Group by date and banner_id, counting total impressions and valid clicks (clicks excluding accidental ones).
Calculate CTR as valid_clicks / impressions. Ensure output includes date, banner_id, impressions, valid_clicks, and CTR, sorted appropriately.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt more manageable after the first part.
First, clarify the metric definition and exclusions (bot traffic, accidental clicks) and confirm the data sources and time window. Then compute user-level 7-day CTR by aggregating clicks and impressions per user over a 7-day period, applying the exclusions. Finally, assign each user to a signup cohort based on the week of their signup date and analyze the distribution of CTR across cohorts, looking for trends or anomalies.
Pro tip: When defining cohorts, ensure you use the user's signup date, not the date of first activity, to avoid misclassification. Also, consider that users in different cohorts may have different exposure to product changes, so interpret trends cautiously and check for confounding factors like seasonality or feature launches.
Define what constitutes a bot and an accidental click, and confirm the exclusion criteria with the interviewer. Specify the 7-day window (e.g., rolling 7 days per user) and the CTR formula: total clicks / total impressions per user.
Identify the relevant tables (e.g., event logs for impressions and clicks, user metadata for signup date). Filter out bot traffic and accidental clicks using the agreed criteria, and ensure data quality (e.g., handle missing values, deduplicate events).
For each user, aggregate clicks and impressions over the 7-day period, then calculate CTR as clicks/impressions. Handle edge cases like zero impressions (exclude or set to null) and ensure the metric is computed per user, not per session.
Determine each user's signup week (e.g., week starting Monday) based on their signup date. Group users into cohorts accordingly, ensuring consistent week definitions across the dataset.
For each cohort, compute summary statistics (mean, median, percentiles) of user-level CTR and visualize the distribution (e.g., box plots, histograms). Compare cohorts over time to identify trends, and consider statistical tests if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Session definition questions always sound clean until you actually try to implement them without loops.
First, clarify the business context and define the pre- and post-periods, the sessionization logic, and the target metrics. Then outline a SQL-based approach to compute per-user average session duration and stories posted in each period, and calculate the change. Finally, discuss how to validate the results and interpret the changes in light of the Group Story feature.
Pro tip: Always check for data quality issues like missing events or users with no sessions in one period, and consider using a robust aggregation method (e.g., median) if the distribution is skewed. Also, segment the analysis by user activity level to avoid Simpson's paradox.
Confirm the exact definitions of pre- and post-periods, sessionization rule (30-minute gap), and the metrics: average session duration per user and stories posted per user. Ensure alignment with stakeholders on edge cases (e.g., users with no sessions).
Write SQL to assign session IDs by flagging gaps >30 minutes between consecutive events per user, then aggregate events into sessions. Compute session duration as the time between first and last event in each session.
For each user, calculate average session duration (mean of session durations) and total stories posted in the pre-period and post-period separately. Handle users with no sessions by excluding them or imputing zero based on business rules.
Compute the per-user change (post - pre) for both metrics. Validate by checking distributions, outliers, and ensuring no data leakage. Consider statistical significance if comparing groups.
Summarize the average change across users, segment by relevant dimensions (e.g., user tenure, engagement level), and relate to the Group Story feature launch. Discuss potential confounders and next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I was least prepared to articulate under pressure.
Start by explaining how you'd refactor the code to use vectorized pandas operations like merge, groupby, and apply with axis=1 only when necessary, and set appropriate indices for efficient lookups. Then describe a testing strategy that validates both correctness and performance using the provided sample data, including edge cases.
Pro tip: Emphasize that vectorization isn't just about avoiding loops—it's about leveraging pandas' optimized C implementations and minimizing memory overhead. Also, mention that setting the right index can turn O(n) lookups into O(1) hash-based operations.
Scan the code for for-loops, iterrows, and apply with axis=1, and replace them with vectorized alternatives like merge, join, groupby, and boolean indexing.
Set indices on columns frequently used for filtering, joining, or grouping to enable fast hash-based lookups and reduce computational overhead.
Use the provided sample data to compare outputs from the original and vectorized versions, ensuring identical results and checking edge cases like missing values.
Measure execution time and memory usage before and after vectorization to demonstrate the efficiency gains, using tools like %timeit or pandas profiling.
Explain any trade-offs such as increased memory usage or reduced readability, and justify why vectorization is still preferable for scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.