Data, SQL & Machine Learning in Practice

Every lesson: deep notes, curated lectures, an animated explainer, a hands-on lab, practice questions and a timed exam.

1. SQL for analysis: SELECT, filters, joins on a real sales database
2. Aggregations, window functions and CTEs
3. SQL from Python, and loading query results into pandas
4. The scikit-learn workflow: splits, pipelines, and leakage-proof preprocessing
5. Linear and logistic regression in practice
6. Decision trees, random forests and gradient boosting
7. Metrics that match the business: precision, recall, ROC-AUC, cost-based thresholds
8. Cross-validation and hyperparameter tuning
9. Feature engineering for tabular data and text (TF-IDF)
10. Clustering and customer segmentation
11. Model cards: reporting what a model can and cannot do
12. Module project

Tools introduced: SQL