Prereq: None. Advanced introduction to theory and application of statistics, data-mining and machine learning using techniques from management science, marketing, finance, consulting, engineering systems, and bioinformatics. Covers bootstrap theory of estimation, testing, nonparametric statistics, analysis of variance, experimental design, categorical data analysis, regression analysis, MCMC, EM, Gibbs sampling, and Bayesian methods. Focuses on data mining, supervised learning, and multivariate analysis. Topics chosen from logistic regression; principal components and dimension reduction; discrimination and classification analysis, trees (CART), partial least squares, nearest neighbors, regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software R and MATLAB. Some background in statistics or instructor permission required. Includes term project.