- Machine learning - Programming

K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S3: Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science, applying knowledge of mathematics, probability and statistics, programming, databases, and languages, grammars and automata. S4: Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques, and applying knowledge of: algebra; geometry; differential and integral calculus; numerical methods; numerical algorithms; statistics and optimization S11: Apply, design, develop, critically analyze and evaluate solutions based on machine learning for applications in specific domains such as recommendation systems, natural language processing, Web or social networks 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 C2: To develop those learning skills necessary to undertake further studies with a high degree of autonomy. 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.

This course aims to serve as an introduction to data science for genomic data. We will introduce basic algorithms for the genome sequencing, comparison of DNA sequences or proteins and the analysis of databases with the genomic profile of different patients for the extraction of information. 1. Computational methods (algorithms and data structures) for analyzing and performing DNA data sequencing. 2. Advanced statistical methods for the analysis of genomic data. 3. Statistical tests for the extraction of conclusions. 4. Projects with real databases

AF1: THEORETICAL-PRACTICAL CLASSES. In them the knowledge that students must acquire will be presented. They will receive the class notes and will have 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 the student and workshops and an evaluation test will be carried out to acquire the necessary skills. AF2: Updated to allegation AF3: STUDENT INDIVIDUAL OR GROUP WORK. AF8: WORKSHOPS AND LABORATORIES. AF9: FINAL EXAM. In which the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: THEORY CLASS. Lectures in class by the teacher with the support of computer and audiovisual media, in which the main concepts of the subject are developed and materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical cases, problems, etc. raised by the teacher individually or in a group. MD3: TUTORING. Individualized assistance (individual tutorials) or in groups (collective tutorials) to students by the teacher. MD6: LABORATORY PRACTICES. Applied / experimental teaching to workshops and laboratories under the supervision of a tutor.