This course aims at presenting state to the art methods used with real data for eliciting important information for decision making. The course will start with basic principles of sampling methodologies and their importance. Then dimension reduction methods like Principal Components Analysis and Factor Analysis and their variants will be discussed. Supervised and Unsupervised Statistical learning methods will follow. For unsupervised methods different types of clustering will be discussed, like partition methods, hierarchical methods and model based methods while problems with large data sets will be illustrated. Supervised learning methods like discriminant analysis, decisions trees, kernel based approaches, nearest neighbors and other classification methods will be also presented. Problems of variables and model selection will be discussed. Finally a brief introduction to Predictive Analytics will be given to elaborate the difference and the importance of predictive approaches in Business analytics. For all topics several examples will be used using R and their libraries.