Scenario library

Your first task: find out how much Delhi revenue fell in September and where the drop came from.

Rank customers by spend with window functions so the CRM team can launch a loyalty programme.

Build a checkout funnel from raw events in Python and find the leak.

Build the monthly deposit report every bank analyst makes — from a very messy core-banking export.

Clean messy loan data and build a simple logistic regression to flag risky borrowers.

Use timestamp maths in SQL to find which departments got slower, and when.

Use CTEs and LEAD() to calculate a key hospital quality metric correctly.

Exploratory analysis in pandas to find where people are leaving and what they have in common.

Calculate CAC and ROI per marketing channel by joining spend with attributed customers.

Slice delivery data by zone, time, weather and kitchen to separate root causes.

Build and honestly evaluate a churn model — and avoid the data leak hiding in the table.

Build a time-series forecast for October that respects trend, weekly patterns and shifting festival dates.