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Coding — SQL, pandas, simulation
Basic aggregates → window functions → multi-CTE → Python / simulation
Why this sequence
SQL warm-ups build muscle for window functions and CTEs. Rolling metrics and cumulative aggregations come next. Multi-part drills consolidate. Python/pandas and numpy simulation close the track. All problems have you solving on a whiteboard in 10 minutes, so brevity matters.
22 questions ·
0 mastered ·
0 in review
Start here
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- 01 NewFind the Most Frequently Co-Purchased Products
- 02 NewFind Users Active in One Period But Not Another (Anti-Join Patterns)
- 03 NewFind the Most Expensive Order for Each User
- 04 NewEarliest Day Each Video Hit 100 Views
- 05 NewTime Between the Last Two Ad Views Per User-Ad Pair
- 06 NewGmail SQL — Top Countries by Email Volume & Month-over-Month Change
- 07 NewTop 100 Week-over-Week User Count Changes
- 08 NewWeek-over-Week Search Growth Rate per Country × Language Segment
- 09 NewRolling Window Active Users — "Users Active in the Last 7 Days"
- 10 NewIn-App Purchase — Multi-Part SQL Drill (Cumulative, Growth Rate, Rolling Average)
- 11 NewTricky Customer Retention Rate with LAG and Multiple CTEs
- 12 NewMinimum Number of Days to Reach Over 1 Billion Unique Users
- 13 NewEstimate the Nth Percentile from Bucketed/Histogram Data
- 14 NewAdd a Conditional Column in Pandas Based on Multiple Other Columns
- 15 NewPandas GroupBy + Visualization
- 16 NewPython Simulation — Average Price Under a Randomized Price Hike
- 17 NewGenerate a Scatter Dataset with Slope ≈ 2 and R² ≈ 0.8
- 18 NewImplement Binary Search on a Sorted Array
- 19 NewFibonacci in Python — Iterative, Memoized, Matrix-Power
- 20 NewFind the K-th Smallest Element With Quickselect
- 21 NewGenerate Samples and Query Distributions in Python (NumPy + SciPy)
- 22 NewSample From a Weighted Discrete Distribution in Python