Master in Data Science

Master in Data Science programme The first year lays foundations in several disciplines. The second year offers a more open curriculum, with access to various courses and workshops.The last semester is dedicated to an internship – either in industry or in research – chosen in line with professional goals.MADS offers refresher courses at the beginning of the Master to help students fill gaps in linear algebra and analysis. Admissions To graduate from the Master in Data Science, one must have acquired 120 ECTS by successfully completing each of the 9 modules in the program. Each module consists of courses that, upon passing the exams, award ECTS. A module is considered validated if either the courses within it are passed or if the grade for each of the exams is above 5, and the weighted average of these grades is at least 10. The program offers a wide variety of pedagogical approaches. Depending on the courses, students may be assessed through written assignments, oral presentations, individual or group projects. The course format is 30, 45, 60 or 90 Teaching Units (TU), with 1 TU equivalent to a duration of 45 minutes. Download the full programme of the academic year 2025-2026 here Academic Contents Semester 1 Semester 1 Semester 2 Semester 3 Semester 4 Course offer for Semestre 1 (2026-2027 Winter) Probability: theory and models ECTS credits: 6 Course code: MA_DS-31 Course title: Probability: theory and models Number of ECTS: 6 Course code: MA_DS-31 Module(s): Module 1 Mathematics for Data Science Language: EN Mandatory: Yes At the end of the course, a student should be familiar with the basic concepts of probability theory (event, random variables, distributions) and master the tools that allow him or her to make calculations (calculations of expectations, variances, distributions etc). The student should also be able to master the law of the large numbers as well as the central limit theorem. He or she must be able to calculate limit distributions of some sequence