This course enables students to learn when and how to apply state of the art data analysis for research and evaluation. Students will gain skills with data analysis tools to perform qualitative, descriptive, inferential, parametric, non-parametric, multifactor and multivariate analysis as well as learn to use graphical data modelling analytic techniques. The course will cover topics such as choosing the right data mining tool and learning how to use linear methods (logistic regression and generalized linear models), and various data mining methods such as: clustering methods, decision trees, multivariate adaptive regression splines, hybrid models, neural networks, support vector machines, bagging and boosting methods. The most recent data mining software will be used to illustrate the methods. The practical part of the course will consists of case studies of health-based data mining projects.