This course aims to equip students with a comprehensive understanding of portfolio risk analytics from a data science perspective.  After building foundational concepts such as estimating portfolio returns and risk, we will shift focus on the various estimation techniques for of covariance matrices including statistically-motivated factor models, economically-motivated factor models, and dimensionality reduction estimators.  With a solid grasp on covariance matrix estimation, the students will then be trained to think in terms of dynamic volatilities and correlations and taught machine learning approaches to forecast these quantities.  Portfolio return distribution estimates and forecasts make up the final part of the course, which touches on topics such as tail risk, Value-at-Risk, expected shortfall, and nonlinear dependence between asset returns.