The Masters in Data Science is organized in 4 semesters and provides an interdisciplinary education in various aspects of Data Science with a strong background in Computer Science and Statistics.
In the first semester, the program includes three levelling courses that allow students with a background in Statistics to obtain complementary knowledge of Computer Science, and vice-versa.
The remaining courses of the 1st semester are elective for all students. The rest of the program has a common structure. It includes 4 compulsory courses (3 during the 2nd semester and 1 on the 3rd), that provide fundamental skills in Computer Science, Statistics and Management. In the 2nd semester, there are two further elective courses and another one during the 3rd semester (second year). The thesis dissertation makes the rest of year 2.
The goal of this Masters degree is to foster in students in a highly interdisciplinary spirit, along with the ability and skills to work and learn autonomously in the crossing areas of Computer Science and Statistics, usually applied to Big Data.
Students that complete the first year of the program will be awarded the title of Specialist in Data Science (which is not a formal degree).
Please consult the Faculty Information System.
The main objective of the Masters is to train highly qualified professionals in Data Science, particularly in the analysis of large volumes of data. The course was designed to provide solid knowledge in the areas of statistical analysis and computer science. Data Science lies in the crossing of these two domains of knowledge that must be mastered by Data Science experts. It is this virtuous combination of skills in these two areas that distinguishes this cycle of studies from other the existing offers that tends to be focused mainly on one of the areas in question. In addition to solid theoretical knowledge on these subjects, the masters conveys practical and applied knowledge in Data Science, through lab classes, practical assignments and projects in collaboration with companies that have real problems that require Data Science methods.
Up-to-date knowledge of the main principles and methodologies of the scientific area ofData Science.
The ability to recognize opportunities of applying Data Science in multiple areas of human activity.
The ability to integrate knowledge, deal with complex issues, develop solutions or make judgments in situations of limited or incomplete information, including reflections on the implications and ethical and social responsibilities.
Ease at communicating results and the knowledge and reasoning underlying such findings, to both experts and non-experts, in a clear and correct manner.
Skills that enable independent lifelong learning.