Weekly Materials
Each week includes learning objectives, perspectival reading with reflection questions, slides, a live notebook demo, and a lab assignment.
| Week | Date | Topic | Slides | Demo | Lab |
|---|---|---|---|---|---|
| Week 1 | Aug 31 | Introduction | Slides | Demo | Lab |
| Week 2 | Sep 7 | Dataframe Basics | — | — | — |
| Week 3 | Sep 14 | Exploring Dataframes — grouping and plotting | — | — | — |
| Week 4 | Sep 21 | Relational Tables — keys, joining and tidying | — | — | — |
| Week 5 | Sep 28 | Clustering & Dimensionality Reduction | — | — | — |
| Week 6 | Oct 5 | Classification basics (kNN) | — | — | — |
| Week 7 | Oct 12 | Decision trees and hyperparameter tuning | — | — | — |
| Week 8 | Oct 19 | Fall Break — no class | — | — | — |
| Week 9 | Oct 26 | Feature Engineering | — | — | — |
| Week 10 | Nov 2 | Linear Regression | — | — | — |
| Week 11 | Nov 9 | Other Models — logistic regression, ensembles | — | — | — |
| Week 12 | Nov 16 | Other Techniques — imbalanced and time-series data | — | — | — |
| Week 13 | Nov 23 | Bias and Fairness | — | — | — |
| Week 14 | Nov 30 | Interpretability Methods | — | — | — |