Stat-ML Lab
·1 min
A large hands-on curriculum for advanced applied statistics and machine learning, built around experiments where possible rather than slideware.
The notebooks cover statistical foundations, A/B testing, causal inference, tabular ML, forecasting, Bayesian methods, SQL and Spark, product analytics, finance and operations research, and ML in production.
Many modules use simulated data with known ground truth so assumptions and failure modes can be tested directly: peeking in experiments, ratio-metric inference, bad controls in causal models, leakage, calibration, drift, backtest overfitting, and more. The goal is to connect mathematical ideas to the judgment required in real systems and senior technical interviews.