Applied Mathematics

1. Knowledge and understanding Students who have passed the exam will know and understand the main tools for Data Analysis: Gaussian models, linear models, principal component analysis, factor analysis, discriminant analysis, analysis of canonical correlation, multi-dimensional scaling, causal models, Markov chains, random graphs, graph-based algorithms. 2. Applied knowledge and understanding Students who pass the exam will be able to solve Data Mining problems, including model selection, prediction, classification, clustering, dimension reduction, feature extraction, causal inference. 3. Making judgments Students will be able to evaluate the results produced by their programs and to produce tests and simulations. 4. Communication skills Students will be able to present and explain the solution of some problems and excercises either at the blackboard and/or using a computer. 5. Learning skills The acquired knowledge will construct the basis to study more specialized topics of Data Science and the numerical methods in this area.