A course in applied statistical computing using software such as R. The primary objective of this course is to strengthen students' skills for modeling data using a variety of statistical learning tools. Key topics introduced include regression, logistic regression, discriminant analysis, model selection and regularization, cross-validation, tree-based methods and clustering.