Prerequisites: STAT 201A; STAT 201B; STAT 202A; graduate standing; or consent of instructor
Description: Covers principle data-mining methodologies and applications. Includes Bayes and LDA classifiers, logistic regression and neural network classifiers, support vector classifiers, classification trees, predictive modeling, ridge and lasso regressions, k-means and Dendrogram clustering methods, business analytics, and mining association rules. Features R and SAS programming languages.
Credit: Credit is awarded for one of the following STAT 208 or ECON 220.