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

K1: Know the principles and values of democracy and sustainable development, in particular, respect for human rights and fundamental rights, gender equality and non-discrimination, the principles of universal accessibility and climate change. K3: Identify and analyze research methodologies and sources to develop academic work in the field of digital information management K7: Understand the fundamentals of statistics and quantitative analysis to interpret data, as well as the appropriate techniques for their collection and processing, understanding different structures, social contexts and user needs. S1: Plan and organize teamwork by making correct decisions based on available information and gathering data in digital environments. S5: Be able to design, manage, and operate with information through database systems, demonstrating skill in information retrieval and the use of query languages to meet complex information needs. S6: Be able to collect, process, cleanse and aggregate data by understanding the needs of users and organizations and how they need them. S7: Experiment with data visualization tools to represent information intuitively, properly presenting the results to different types of audiences. S10: Apply statistical analysis techniques and metric studies to evaluate and measure the impact of data in digital environments. C1: Know and be able to manage interpersonal skills on initiative, responsibility, conflict resolution, negotiation, among others, which are required in the professional field. C2: Be able to apply knowledge in a professional way in solving specific digital information management problems using the tools and techniques learned in the academic field C3: Demonstrate ability in the development and execution of digital content projects autonomously working in multidisciplinary teams. C4: Capacity for continuous autonomous learning that facilitates adaptation to new situations and the updating of knowledge in the field of digital information.

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.