| M March 30 | Introduction | slides | | Data 8 Ch 1 |
| W April 1 | EDA & visualization | slides | | IMS Ch 2 |
| M April 6 | Data munging + AI gotchas | slides | | Pandas tutorials |
| W April 8 | Linear models: from data to geometry | slides | Quiz 1 | VMLS Ch 1, 5 |
| F April 10 | | | HW1 | |
| M April 13 | Feature engineering and regression diagnostics | slides | | VMLS Ch 13; Data 8 Ch 15 |
| W April 15 | Validation and the bias-variance tradeoff | slides | Quiz 2 | VMLS Ch 13.3; IMS Ch 8–10 |
| F April 17 | | | Project groups | |
| M April 20 | Classification (logistic regression + metrics) | slides | | IMS Ch 9 |
| W April 22 | Bootstrap and the normal approximation | slides | Quiz 3 | Data 8 Ch 10; IMS Ch 5 |
| F April 24 | | | HW2 | |
| M April 27 | Permutation tests | slides | | Data 8 Ch 12; Data 8 Ch 13 |
| W April 29 | Hypothesis testing framework | slides | Quiz 4 | Data 8 Ch 11 |
| F May 1 | | | Project proposal | |
| M May 4 | Multiple testing | slides | | Poldrack Ch 17; Data 8 Ch 15.1 |
| W May 6 | Regression inference + diagnostics | slides | Quiz 5 | IMS Ch 24–25 |
| F May 8 | | | HW3 | |
| M May 11 | Decision trees and random forests | slides | | IMS Ch 8–10 |
| W May 13 | PCA / dimensionality reduction | slides | Quiz 6 | VanderPlas Ch 5.09 |
| F May 15 | | | Project midterm report | |
| M May 18 | Clustering (k-means) | slides | | VMLS Ch 4.1 |
| W May 20 | When validation isn’t enough | | Quiz 7 | FPP3 Ch 5 |
| F May 22 | | | | |
| M May 25 | Holiday | | HW4 | |
| W May 27 | AutoML, LLMs, and the future of data analysis | | Quiz 8 | TBD |
| M June 1 | Causal inference I: DAGs + confounding | | HW5 | The Effect Ch 1–6 |
| W June 3 | Causal inference II: natural experiments + A/B tests | | | The Effect Ch 16–18 |
| F June 5 | Final exam 3:30–5:00 PM | | | |
| M June 8 | | | Project final report + peer evaluations (9 AM) | |