Postgrad
Self-Directed Post-Graduate Program in Applied Economics & Causal Data Science
After graduation, I sought a master's program that could fuse the causal reasoning of economics with the predictive power of modern data science. Finding none that fit this vision, I engineered my own. This self-directed specialization became the direct foundation for my career, enabling me to first build and lead the data science unit at ECES—where my team leverages Causal and Generative AI to drive policy impact. This expertise also allowed me to advise Fortune 500 companies on solving their most critical operational and manufacturing challenges.
The multi-year curriculum that enabled this work is detailed below, structured from foundational principles to advanced applications and complete with links to the core courses and books I used.
1. Python
2. Calculus
3. Linear Algebra
4. Statistics
5. Machine Learning
6. Deep Learning
7. Causal Inference
8. Econ Development
9. Bayesian Statistics