The introductory block of courses contains a course in basic statistics offered for students with a background in computer science or engineering, and a course in programming offered for students having a degree in statistics or mathematics. The courses Introduction to Machine learning, Data Mining, 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.
- Statistical methods, 6 credits
- Advanced R programming, 6 credits
- Advanced Academic studies, 3 credits
- Introduction to Machine Learning, 9 credits
- Advanced Data Mining, 6 credits
- Big Data Analytics, 6 credits
- Introduction to Python, 3 credits
- Philosophy of Science, 3 credits
- Bayesian Learning, 6 credits
- Computational statistics, 6 credits
- Time series analysis, 6 credits
- Multivariate Statistical Methods, 6 credits
- Web Programming, 6 credits
- Neural networks and learning systems, 6 credits
- Visualization, 6 credits
- Advanced Machine Learning, 6 credits
- Probability Theory, 6 credits
- Decision Theory, 6 credits
- Data Mining Project, 6 credits
- Text mining, 6 credits
- Database Technology, 6 credits