Probability and Data Analysis

LEARNING OUTCOMES RA1:Students should have acquired advanced knowledge and demonstrated an understanding of the theoretical and practical aspects and working methodology in the field of data science and engineering with a depth that reaches the forefront of knowledge RA2:Be capable of applying their knowledge and problem-solving skills, through arguments or procedures developed and sustained by themselves, in complex or professional and specialized work settings that require the use of creative and innovative ideas. RA3:Have the ability to collect and interpret data and information on which to base their conclusions including, where appropriate and pertinent, reflection on issues of a social, scientific or ethical nature within their field of study GENERAL COMPETENCES CG4:Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science CG5:Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques. SPECIFIC COMPETENCES CE5:Ability to understand and manage fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them CE12:Ability to model, predict, filter, and smooth random signals and stochastic processes

1. Introduction to Stochastic Processes 2. Discrete Markov Chains 3. Continuous time Markov Chains 4. Renewal Processes 5. Queuing theory 6. Random Graphs 7. Case studies: Monte Carlo Algorithm, PageRank Algorithm, Call centers, Social networks.

Theory (4 ECTS). Theory classes with additional material available on the Web. Practical classes (2 ECTS) Problem solving classes. Problem based learning classes.