老澳门六合彩开奖记录

Geology students reviewing a topographical map. background

Course Catalog

Predictive Analytics

Students will learn foundational concepts and techniques in statistical learning focusing on building and evaluating models for regression and classification problems. Topics will include data preprocessing, error/accuracy metrics, data splitting, cross-validation, and model selection. These topics will be presented through the use of linear/logistic regression, penalized regression, k-nearest neighbors, and tree-based models. Additional topics may include, but are not limited to, clustering, neural networks, PCA, PCR, support vector machines, and splines/smoothing. Students will work on projects requiring them to select and use appropriate tools for solving open-ended problems. Communication of results to various audiences will be emphasized. Prerequisites: DATA 201, and MATH 203 is recommended.

Grade Basis: Letter Grade

Credits: 4.0