Concepts and techniques in the field of statistical modeling will be introduced. Materials include the models used to explore different sorts of data for the discovery of predictive models and knowledge. The course will include models such as linear regression models, logistic regression models, support vector machines, decision trees, random forest, ensemble models, nearest neighbor, clustering, as well as spatial models, time-series models, quantile regression models, and network models. R or Python will be covered. Students will also gain hands-on experience with data analytical tools from various applications in sciences.