Probability and data analysis Statistical learning Predictive modeling

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 RA4:Be able to cope with complex situations or those that require the development of new solutions in the academic, work or professional field within their field of study RA5:Know how to communicate to all types of audiences (specialized or not) in a clear and precise manner, knowledge, methodologies, ideas, problems and solutions within the scope of their field of study RA6:Be able to identify their own training needs in their field of study and work or professional environment and organize their own learning with a high degree of autonomy in all types of contexts (structured or not). BASIC COMPETENCES CB3:Students have the ability to gather and interpret relevant data (usually within their field of study) in order to make judgements which include reflection on relevant social, scientific or ethical issues. CB5:Students will have developed the learning skills necessary to undertake further study with a high degree of autonomy GENERAL COMPETENCES CG2:Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations 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 CG6:Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally SPECIFIC COMPETENCES CE6:/Ability to acquire the fundamentals of Bayesian Statistics and learn the different techniques of intensive computing to implement Bayesian inference and prediction CE7:Ability to assimilate basic concepts of programming and ability to perform programs oriented to data analysis

1. Random number generation 2. Monte Carlo methods a. Rejection method b. Importance sampling 3. Markov Chain Monte Carlo Methods a. Metropolis Hastings algorithm b. Gibbs sampling c. Slice sampling 4. Resampling methods a. Bootstrap b. Jacknife c. Randomization tests d. Cross-validation

AF1: THEORETICAL-PRACTICAL LESSONS where the knowledge that students should acquire is presented. Students will receive class notes and basic reference texts to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems will be solved by students and workshops and evaluation tests will be held to acquire the necessary skills. AF3: INDIVIDUAL OR GROUP WORK OF THE STUDENT. AF9: FINAL EXAM where the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: CLASS THEORY. Presentations offered by the teacher in class with computer support and audiovisual media, where the main concepts of the subject are developed and materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical case studies, problems, etc. proposed by the teacher individually or in groups. MD3: TUTORIALS. Individualized assistance (individual tutorials) or group (collective tutorials) offered to students by the teacher.