The programme is organised to give a solid foundation in data science while allowing room to specialise. Core qualification modules cover the fundamental methods and concepts of data science. You then choose from three specialisation areas—mathematics, computer science and applications—to concentrate on particular aspects of the field; you may emphasise one area, but you must take at least one module from each of the three. Complementing the technical content are general qualification modules that cover non-technical subjects such as business and management, research methods, and a seminar component.
Project work and the Master’s thesis are embedded in the university’s research environment: both can be carried out at different institutes within the university, enabling students to pursue research-oriented topics and to collaborate with active research groups. Teaching emphasises the methodology and logic of engineering—“learning to think”—so students build a strong theoretical base that is balanced by practical experience through internships, project work and the thesis.
The programme also prepares students for international and interdisciplinary work. You will be expected to learn and work independently and in multicultural teams, and students are integrated into research and development projects early on to ease the transition into professional life or doctoral research.
Requirements (academic/programme structure)
Language of instruction: English.
Learning outcomes include rigorous methodological thinking, the ability to design and implement data-driven solutions, competence in interdisciplinary teamwork, and readiness for research or industry roles.
Applicants must hold a Bachelor's degree (or an internationally recognized equivalent) in a data science programme or in a closely related field. If your undergraduate degree is not explicitly in “Data Science,” you should be able to demonstrate that your prior studies included a solid foundation in computer science and mathematics.
“Equivalent programme” typically covers degrees such as Computer Science, Mathematics, Statistics, Engineering, Physics or similar disciplines — provided the curriculum contains substantial computing and math content. Admissions will look for evidence of relevant coursework and skills rather than the exact degree title, so transcripts and course descriptions can be important for evaluation.
To help you prepare, common indicators of a sufficiently strong background include prior coursework and practical skills in programming, algorithms/data structures, linear algebra, calculus, and probability & statistics. If your degree and transcripts clearly show competence in these areas, you are likely to meet the program’s foundational expectations.
Winter Semester (International)
1 March 2026
Winter Semester (EU/EEA)
15 July 2026
Graduates are prepared for careers as data scientists, machine learning engineers, data analysts, or research assistants in industry and academia. The programme’s mix of theory, applied projects and research experience equips students to take on roles that require solid methodological understanding, software and algorithmic skills, and the ability to work in interdisciplinary teams.
The emphasis on independent problem-solving and integration into research groups also makes graduates competitive candidates for PhD programmes or R&D positions where advanced analytical and research skills are required.
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