Data Mining And Statistical Learning

Catalog description

This is an introductory course in data science with a major focus in statistical learning. This course introduces methodology, software tools, and real-life applications in data mining. It covers supervised learning methods including ridge regression, the lasso and elastic net regression, regression splines, principal component regression, resampling methods, classification, tree-based methods, support vector-machines and unsupervised learning methods including principal component analysis and clustering. Prerequisite(s): STAT 43000 FOR LEVEL UG WITH MIN. GRADE OF C- (MAY BE TAKEN CONCURRENTLY) Course Learning Outcomes 1. Distinguish between supervised and unsupervised learning methods. 2. Solve data mining problems with various modern statistical techniques. 3. Evaluate different statistical learning methods and identify the optimal choice. 4. Employ various statistical techniques using statistical software. View Class Schedule