Sessions 1-4: Learn techniques quickly. Session 5A: Solve one complex business problem deeply.
The Goal: 70% completion. Students explore data, build predictive models, identify slow-moving inventory, and recommend business actions.
The Timeline: TEACH (30 min) → BUILD (120 min) → SHOWCASE (30 min)
Analyzing 90 days of sales data, you discover: winter coats are slow in September (expected, do nothing) but premium shoes are slow even in season (pricing issue, markdown them 20%).
That's business intelligence. Using data to make better decisions.
Normal. Do nothing. Winter coats in Sept = expected.
Fixable. Markdown. Premium shoes at $220 = too expensive.
Fixable. Move it. Hard-to-find items = visibility issue.
Example: Winter Coat (seasonal - protect), Premium Shoes (pricing - markdown), Youth Size (placement - move to end-cap).
Student has hypothesized the root cause and recommended actions with business logic.
This is the 70% goal. Session 5B polishes it to 100%.
✓ Show an example analysis (what 70% looks like)
✓ Celebrate their progress ("Look at what you've done")
✓ Normalize doubt ("You're learning hard stuff")
✓ Keep momentum ("30 minutes left")
✓ Chunk the remaining work into small wins
Real dataset with 8,000+ rows
Handled missing values, identified outliers
ML model identified feature importance
Found patterns and insights
3-5 slow movers with recommendations
Real work to show employers