Schedule

4-Week Intensive Course Schedule (Summer 2026)

Course Format: 20 business days (Mon-Fri), May 18 - June 14, 2027 Daily Engagement: 112.5 minutes (videos + Colab notebooks + exercises + quizzes + project work) Instruction Style: Micro-videos (≤12 min) + Google Colab notebooks with Gemini AI assistance

Day	Date	Topic	Videos	Notebook	Assessment	Materials
0	Pre-course	Launchpad: Welcome, course setup, and Colab orientation	2 videos (10 min)	00_launchpad_course_setup_student.ipynb	Colab Readiness Check	Google Colab docs
1	Tue May 18	Predictive analytics fundamentals, EDA, and data splitting	5 videos (48 min)	01_eda_splits_student.ipynb	Concept Quiz	ISLP Ch 2 sklearn: cross-validation Kaggle Learn: data leakage
2	Wed May 19	Data setup and preprocessing pipelines (the professional way)	6 videos (54 min)	02_preprocessing_pipelines_student.ipynb	Concept Quiz Notebook Checkpoint	sklearn: pipelines, ColumnTransformer Pedregosa et al.: scikit-learn paper
3	Thu May 20	Train/validation/test rigor + regression metrics + baseline modeling	6 videos (54 min)	03_regression_metrics_baselines_student.ipynb	Concept Quiz 3-sentence Evaluation Note	ISLP: Model Assessment sklearn: regression metrics
4	Fri May 21	Linear regression that actually works: features, interactions, diagnostics	6 videos (54 min)	04_linear_features_diagnostics_student.ipynb	Concept Quiz Notebook Checkpoint	ISLP Ch 3: Linear Regression sklearn: LinearRegression, PolynomialFeatures
5	Mon May 24	Regularization (Ridge/Lasso) + Project proposal sprint	6 videos (48 min)	05_regularization_project_proposal_student.ipynb	Concept Quiz PROJECT MILESTONE 1: Proposal + Dataset	ISLP Ch 6: Regularization sklearn: Ridge/Lasso/ElasticNet
6	Tue May 25	Logistic regression: probabilities, decision boundaries, and pipelines	6 videos (54 min)	06_logistic_pipelines_student.ipynb	Concept Quiz Notebook Checkpoint	ISLP Ch 4: Classification sklearn: LogisticRegression
7	Wed May 26	Classification metrics: confusion matrix, ROC/PR, calibration, and business costs	6 videos (54 min)	07_classification_metrics_thresholding_student.ipynb	Concept Quiz Threshold Recommendation	Fawcett: ROC analysis Saito & Rehmsmeier: PR curves sklearn: classification metrics
8	Thu May 27	Resampling and CV: how to compare models without fooling yourself	6 videos (54 min)	08_cross_validation_model_comparison_student.ipynb	Concept Quiz Notebook Checkpoint	ISLP Ch 5: Resampling sklearn: cross-validation utilities
9	Fri May 28	Feature engineering + model selection workflow (and Project baseline build)	6 videos (48 min)	09_tuning_feature_engineering_project_baseline_student.ipynb	Concept Quiz Project Baseline Draft	sklearn: GridSearchCV Provost & Fawcett: evaluation framing
10	Mon May 31	Midterm: Business-case predictive strategy practicum + Project baseline submission	6 videos (30 min)	10_midterm_casebook_student.ipynb	MIDTERM (graded) PROJECT MILESTONE 2: Baseline Model + Evaluation Plan	Provost & Fawcett: business framing sklearn: common pitfalls
11	Tue June 1	Decision trees: interpretable models with sharp edges	6 videos (54 min)	11_decision_trees_student.ipynb	Concept Quiz Notebook Checkpoint	ISLP Ch 8: Tree-Based Methods sklearn: DecisionTree estimators
12	Wed June 2	Random forests: bagging, OOB intuition, and feature importance	6 videos (54 min)	12_random_forests_importance_student.ipynb	Concept Quiz Notebook Checkpoint	Breiman: Random Forests paper sklearn: RandomForest, permutation importance
13	Thu June 3	Gradient boosting: performance with discipline (and leakage avoidance)	6 videos (54 min)	13_gradient_boosting_student.ipynb	Concept Quiz Notebook Checkpoint	Friedman: Gradient Boosting Machine sklearn: gradient boosting estimators
14	Fri June 4	Model selection and comparison: making the call like a professional	6 videos (54 min)	14_model_selection_protocol_student.ipynb	Concept Quiz Notebook Checkpoint	ISLP: Model Assessment sklearn: model evaluation best practices
15	Mon June 7	Interpretation: feature importance + partial dependence + project improved model delivery	6 videos (48 min)	15_interpretation_error_analysis_project_student.ipynb	Concept Quiz PROJECT MILESTONE 3: Improved Model + Interpretation	sklearn: inspection tools (PDP, permutation) Molnar: Interpretable ML (optional)
16	Tue June 8	Error analysis to decisions: thresholds, calibration, and KPI alignment	6 videos (54 min)	16_decision_thresholds_calibration_student.ipynb	Concept Quiz Decision Policy Paragraph	Provost & Fawcett: decision-making sklearn: calibration tools
17	Wed June 9	Fairness and ethics basics: responsible predictive analytics (minimum viable rigor)	6 videos (54 min)	17_fairness_slicing_model_cards_student.ipynb	Concept Quiz Model Card Draft	Barocas et al.: Fairness and ML Mitchell et al.: Model Cards
18	Thu June 10	Deployment thinking: reproducibility, monitoring, drift, and “don’t ship a notebook”	6 videos (54 min)	18_reproducibility_monitoring_student.ipynb	Concept Quiz Notebook Checkpoint	Chip Huyen: Designing ML Systems sklearn: model persistence
19	Fri June 11	Executive narrative: slide-style story + conference video plan (project studio)	6 videos (42 min)	19_project_narrative_video_studio_student.ipynb	Concept Quiz Draft Slide Outline + Script	Knaflic: Storytelling with Data Minto: Pyramid Principle
20	Mon June 14	Final delivery: project package submission + peer review + course closeout	6 videos (30 min)	20_final_submission_peer_review_student.ipynb	PROJECT MILESTONE 4: Final Deliverable (notebook + deck + video) Peer Review (graded) Final Concept Quiz	Mitchell et al.: Model Cards Chip Huyen: deployment checklists

Note: All notebooks are designed to run in Google Colab with Google Gemini AI assistance for guided “vibe coding” (draft → verify → document).

Project Milestones

• Week 1 (Day 5): Proposal + dataset selection (1-page proposal + dataset link + target + metric + split plan + leakage risks)
• Week 2 (Day 10): Baseline model + evaluation plan (baseline pipeline + metric + split/CV design + baseline report table)
• Week 3 (Day 15): Improved model + interpretation (updated comparison, champion choice, importance + PDP/ICE, error segment findings)
• Week 4 (Day 20): Final model + executive-ready deliverable (final run-all notebook + model card/limitations + monitoring plan + slide narrative + conference-style video)

Core Course References

• James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning (ISLP) + Python labs. Download: https://www.statlearning.com/
• Hastie, Tibshirani, Friedman. The Elements of Statistical Learning (ESL).
• Provost, Fawcett. Data Science for Business.
• Pedregosa et al. “Scikit-learn: Machine Learning in Python.” JMLR.
• scikit-learn User Guide (pipelines, preprocessing, model selection, metrics, inspection).
• Chip Huyen. Designing Machine Learning Systems (deployment thinking, monitoring).