The aim of the course is to introduce data science concepts and algorithms, which are less covered in the Introduction to Data Science course, in a practical approach based on the mathematical knowledge acquired previously. Subjects: Data manipulations Predictiv analysis Visualization with real datasets Python packages: pandas, scikit-learn, matplotlib, ggplot Introduction to R and other data sciece related tools Bayesian network Collective methods for clustering: random forest bagging, boosting Clustering: DBSCAN, EM algorithms Recommendation systems Assiciation rules Anomalies (outlier) detection Large scale case studies