1. Introduction to multivariate data with R. 2. Data visualization: principal component analysis. 3. Data visualization: metric multidimensional scaling. 4. Classification: linear discriminant analysis. 5. Classification: logistic regression. 6. Classification: Naive Bayes classifier. 7. Data segmentation: cluster analysis. Hierarchical models and k-means algorithm. 8. Introduction to prediction with non linear data