This course introduces students to statistical tools for modeling and identifying patterns in complex data sets. The goal of statistical learning is to develop predictions informed by data. Topics to be covered include Gaussian linear models, cross-validation techniques, penalized regression methods such as ridge and LASSO, nonlinear models, logistic regression, tree-based models including random forests, bagging, and boosting, and support vector machines. Application areas include Marketing (e.g., effectiveness of advertising and customer satisfaction), Financial Economics (valuation), and Operations Management (resource allocation). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analyses.