The introductory block of courses contains a course in basic statistics that is recommended for students with a background in computer science or engineering, and a course in programming that is recommended for students having a degree in statistics or mathematics. The courses Machine learning, Advanced Data Mining, Deep Learning, Big Data Analytics, Computational Statistics and Bayesian learning constitute the core of the programme.
In addition, master’s students have the freedom to choose among profile courses - aimed to strengthen students’ statistical and analytical competence - and complementary courses - that allow students to focus on particular applied areas or relevant courses from other disciplines. Opportunities for exchange studies are provided during the third semester of the programme.
To be awarded the degree, students must have passed 90 ECTS credits of courses including 42 ECTS credits of the compulsory courses, a minimum of 6 ECTS credits of the introductory courses, a minimum of 12 ECTS credits of the profile courses, and, possibly, some amount of complementary courses. The students must also have successfully defended a master’s thesis of 30 ECTS credits.