SQL Case Study for a Data Science Problem

Summary

Apply SQL to build an actual training-ready dataset — pulling, joining and aggregating raw tables into a clean, feature-ready table for a hypothetical ML problem.

• 1Pick a hypothetical prediction problem (e.g. churn) and define what features you'd need.
• 2Write the SQL to pull and aggregate those features from a multi-table sample dataset.
• 3Document your query logic so someone else could reproduce the same feature set.