DATA 356 - Applied Regression Analysis

3 Credits
This course introduces students to regression-based modeling. It covers supervised versus unsupervised learning, bias-variance tradeoff, cross-validation, simple and multiple linear Least Squares regression, variable selection methods, ridge regression, and Lasso. Emphasis is placed on model creation and validation rather than traditional inference methods. Students get hands-on practice by conducting a data analytics project using real world data sets.

Prerequisite(s): DATA 275