At the end of the course students will be able: To understand the importance of data science in today's knowledge society. Use data visualization techniques to understand the problems faced by a data scientist and to report the results obtained. To know when to use a supervised or an unsupervised data analysis technique. To know the main data analysis techniques and applications where they have been used successfully. To know the main problems a data scientist may encounter and how to deal with them To know the different actual data analysis tools. To perform an basic data analysis using R-Studio.

1. The importance of Data Science 2. Introduction to R-Studio 3. Understanding the data: Case studies of exploratory data analysis and visualization techniques I 4. Understanding the data: Case studies of exploratory data analysis and visualization techniques II 5. Importance of a good design of experiments and choice of performance measures: precision, sensitivity, specificity. Over-fitting 6. Introduction to supervised classification: case studies on decision trees and random forests 7. Introduction to unsupervised techniques: case studies of clustering methods

The course is taught in 14 theoretic-practical lessons and 14 practical lessons. The subject is mostly practical, and for this reason in the master classes the main theoretical concepts of the subject will be explained, but they will also be put into practice with computer exercises. These concepts will be further elaborated in the practical classes in which various computer-based data analyses will be carried out. The students will also have office hours where they will have the opportunity to resolve any doubts they may have about the theoretical and practical classes or about the assignments they have to carry out.