An introduction to the essential tools and techniques of data mining/machine learning. Students will learn (1) how to execute the key steps in the data mining process - including data preprocessing, data exploration and visualization, supervised and unsupervised learning, model selection and validation, and complexity control; and (2) how to build reliable predictive models. The data mining methods covered are Linear Regression, Logistic Regression, K-nearest neighbors, Classification and Regression Trees, Ensemble methods, K-Means and Hierarchical Clustering, and Association Rules. The focus will be on business applications - realistic data from Marketing, Finance, Operations, and other functional areas will be used to illustrate the breadth of applications.