AI and Data Scientist Roadmap

1. What a Data Scientist Does
   Communicating Results (recommended)Ethics & Bias in Models (recommended)Handoff to Production (alternative option)
2. Probability Fundamentals (recommended)Correlation vs. Causation (recommended)
   Statistics Foundations
3. Experimental Design
   A/B Testing Analysis (recommended)
4. Feature Selection (alternative option)
   Feature Engineering
5. Supervised Learning
6. Overfitting & Regularization (recommended)
   Model Evaluation
7. Unsupervised Learning
   When to Reach for Deep Learning (alternative option)
8. Notebooks & Reproducibility (alternative option)
   Core Tooling (scikit-learn, statsmodels)

Keep learning — keep building.