Statistical Techniques and Decision Modeling

Catalog description

This course develops an understanding of core and advanced statistical concepts used in data science. It builds on core statistical concepts covered in other foundational statistics courses. Topics include hypothesis testing, classical and Bayesian statistical inference, multiple regression, logistic regression, analysis of variance, and non-parametric methods. The course also introduces students to decision modeling techniques including Monte Carlo simulation, linear and non-linear optimization, decision trees, and risk analysis. The course includes hands-on exercises. P: 15 credits of COMP SCI, INFO SCI, or COMM Spring.