This course will introduce modern predictive and learning analytics techniques. The main emphasis will be on the applied aspects of these techniques with programming in the R language (an open source software that has gained tremendous popularity recently). Each lecture is designed to introduce new methods followed by real data applications from various applied fields (marketing, operations, finance, economics, and sports analytics). In introducing these predictive analytics tools, the course will feature discussions on four broadly defined areas of focus: 1) finding the most appropriate model that best represents the data, 2) selecting the optimal set of predictors, 3) reducing the dimension of data and dealing with correlated predictors, 4) improving prediction performance. A summary of topics that will be covered in the course is as follows: linear and non-linear regression analysis (ridge, Lasso, K-nearest neighbor, non-linear splines, neural networks), classification methods (logistic regression, linear discriminant analysis, support vector machines), tree based methods (regression/classification trees, bagging, boosting, random forests), unsupervised learning methods (principle components analysis, k-means clustering, hierarchical clustering).