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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

Practical Rebalancing, Bayesian Views, and Robustness

Module 5

Alternative Portfolio Objectives and Risk Allocations

Module 6

Backtesting Without False Discoveries

Module 7

Factor Models, Attribution, and Stress Testing

Module 8

Machine Learning Signals for Portfolio Construction

Module 9

Reproducible Quant Research Engineering

Module 10

Capstone Evidence and Hiring-Ready Communication