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.

K5: Ability to understand and relate fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them K7: Assimilate basic concepts of programming, including control structures, data types, and functions, and their application in developing programs for data analysis, processing, and visualization in the field of Data Science and Engineering. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S2: To use information interpreting relevant data avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to assess the reliability and quality of such information. S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences C5: Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.

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.