This course provides an introduction of the up-to-date predictive analytics used for statistical/machine learning. It covers topics such as basics of statistical learning, linear models, time series models, resampling methods, principal components analysis, decision trees, and cluster analysis, with practical applications in R. The lecture portion of this course is designed to help students understand the fundamental knowledge about predictive analytics for risk modeling, while the laboratory component of this course is to complement the lecture materials and to provide students with practical experience in statistical learning and programming with R language.