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Stripe Interview Problems (48)

Problems reported from Stripe data science interviews

Status Title Difficulty
RANK and DENSE_RANK Comparison Pro Medium
Regression Homoscedasticity Pro Medium
Running Revenue by Salesperson Pro Medium
Top N Per Group with ROW_NUMBER Pro Medium
Total Spend Per Customer Pro Medium
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Unmatched Records: Orders Without Customers Pro Medium
Amortized Analysis Pro Hard
AUC-ROC Interpretation Pro Hard
Cumulative Sum Window Pro Hard
Dealing with Data Quality Issues Pro Hard
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Distributed Computing Challenges Pro Hard
Handling Class Imbalance with SMOTE Pro Hard
Join with Aggregation and Filtering Pro Hard
Latest Order Per Customer Using Join and Subquery Pro Hard
NTILE for Quartile Bucketing Pro Hard
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Orders Exceeding Customer Lifetime Average Pro Hard
Percentile Rank of Order Values Pro Hard
REST API Design Principles Pro Hard
Revenue Contribution by Region With Rollup Pro Hard
ROI Calculation for a Data Project Pro Hard
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Running Total Pro Hard
Running Total of Order Amounts Per Customer Pro Hard
Target Encoding Pro Hard
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