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Data Science and Optimization

Module 1

Module 2

Estimating Returns and Risk Under Sampling Error

Module 3

Mean–Variance Optimization from Mathematics to Code

Module 4

Rebalancing, Bayesian Views, and Robust Optimization

Module 5

Alternative Portfolio Objectives and Risk Allocations

Module 6

Backtesting and Walk-Forward Model Selection

Module 7

Factor Models and Factor-Aware Portfolio Construction

Module 8

Benchmarking, Attribution, Statistical Evidence, and Stress Testing

Module 9

Machine Learning Signals for Portfolio Construction

Module 10

Reproducible Quantitative Research Engineering

Module 11

Capstone Evidence, Communication, and Interview Readiness