Description

Theory
2

Laboratory
3

Instructors

Sandra Ramos

Contents

SY1. Fundamentals: data collection, processing and selection; types of variables; visualization;
SY2. Data analysis from the perspective of statistics and machine learning;
SY3. Statistical methods of regression and classification;
SY4. Machine learning algorithms of classification, regression and clustering.

Learning Outcomes

The general objective of this curricular unit is to provide the student with the knowledge and tools necessary to extract knowledge/information from the data and to be able to use it for informed decisions. This general objective is based on the assumption that the student should be able to:

LO1. Know the different types of data and different processes of collection;
LO2. Use processes of representation and visualization of data;
LO3. Work with multivariate data and use dimensionality reduction techniques;
LO4. Understand different statistical techniques for regression and classification and know how to use them to solve problems of various kinds;
LO5. Know machine learning algorithms and know how to use them to solve supervised and unsupervised classification and regression problems;
LO6. Develop group work and produce technical reports.