Master of Science in Data Science

The two-year Master's programme in Data Science is built on the Data Science BSc programme and can be started in either the summer or winter semester. It is a joint project of the Departments of Statistics, Computer Science and Mathematics. As the tasks are always rapidly changing in this research field, the study programme provides a sound statistical and mathematical theory and teaches contemporary methods and knowledge. Hence, the students learn to be capable of doing scientific research. They develop scientific awareness and can use it responsibly. The interdisciplinary ability to communicate about methods and their applications, especially at the interface between statistics, computer science, mathematics and fields of application, is a key aspect of the study programme. Our programme builds on our six-semester Bachelor's in Data Science (taught in German). Unlike many other MSc Data Science programmes that devote the first semester to revisiting foundational topics in mathematics, statistics, and computer science, we do not offer a dedicated review semester. This is because our students join the programme with a strong background from our Bachelor's curriculum. As a result, we expect incoming students to have solid prior knowledge. If you find gaps in your understanding, it is your responsibility to address them independently. The advantage of this approach is that we can focus more of the programme on specialised Master’s-level content. Our curriculum places particular emphasis on advanced statistics, with a strong focus on the development of underlying statistical theory. The programme offers a variety of core and elective modules, allowing students to tailor their studies to their individual interests. A broad selection of courses is available to choose from. Internships, project-based courses, and the Master's thesis provide opportunities for students to apply their knowledge in practice. Building on prior studies, the advanced modules cover topics such as statistical theory, statistical learning, big data, and the application of methods in selected fields (through case studies, seminars, and elective courses). Particular emphasis is placed on developing and applying efficient techniques for analysing large-scale data. PDF Download