Basic multivariate analysis

1. To know and use advanced statistical techniques, with last generation software support. 2. To extract and analyze information from large data sets. 3. Learning the basic Statistical skills for the analysis of multivariate socio-economical data such as those coming from a market research. 4. Being able to describe and analyze real data sets using the techniques mentioned above. 5. Being able to elaborate reports with the results of the analysis of real case studies. 1. Information analysis and synthesis capacity on data mining problems. 2. Solving real problems. 3. Learning and training in the use of Statistical software to solve real case studies. 4. Critical and selective reasoning to solve

1. R programing language 1.1 Data types and importing data 1.2 Loops and conditionals 1.3 Functions 2. Exploratory Data Analysis 2.2 Ggplot2 package 3. Supervised Classification 3.1 K-nearest neighbors 3.2 Decision Trees 3.3 The Gaussian distribution and discriminant analysis 3.4 Support Vector Machines 3.5 Logistic Regression 4. Dimensionality Reduction and clustering techniques 4.1 Principal Component Analysis 4.2 K-means 4.3 Hierarchical Clustering 5. How to write a report with R-Markdown

The program consists of 14 theoretical classes with supporting material available in the global classroom and 14 sessions based on computer practice sessions. Every week students will have an optional collective tutorial where they can solve their doubts.