Who this track is for
The Data Scientist track is for analysts and engineers who want to build models that drive real decisions. You’ll go deep on statistical foundations, experimental design, and the full ML lifecycle — from feature engineering to production monitoring. Best fit if you:- Come from an analytics, research, or quant background
- Want to run experiments and build models at product companies
- Are interested in the why behind ML, not just the how
Curriculum
Level 1 — Statistical Foundations (free)
| Module | Key Topics |
|---|---|
| Advanced Statistics | Bayesian inference, distributions, sampling, CLT |
| Experimental Design | A/B testing, power analysis, p-values, practical significance |
Level 2 — Machine Learning
| Module | Key Topics |
|---|---|
| Supervised Learning Deep Dive | Regression, trees, ensembles, model selection |
| Feature Engineering | Encoding, imputation, scaling, feature importance |
| Model Evaluation | Cross-validation, ROC/AUC, calibration, leakage |
Level 3 — Production ML
| Module | Key Topics |
|---|---|
| ML Pipelines | sklearn pipelines, preprocessing, reproducibility |
| Model Deployment | FastAPI, batch inference, model registries |
| Monitoring & Drift | Distribution shift, data quality, alerting |
Level 4 — Advanced Topics
| Module | Key Topics |
|---|---|
| Time Series | Forecasting, seasonality, ARIMA, Prophet |
| NLP Fundamentals | Text classification, embeddings, transformers for DS |
| Causal Inference | Diff-in-diff, IV, propensity scoring |