Introduction to data analytics and information visualization; methods and metrics for validation; bias-variance trade- off; data visualization and understanding; data preprocessing; supervised learning (classification and regression); unsupervised learning; association rule mining; feature subset selection; metaheuristics; PCA; ANN and Multilayer Perceptron.