This English-taught master's programme deepens your knowledge and provides specialisation in handling (big) data, responding to the evolving demands of a digitalised society and a changing professional landscape. Through a transdisciplinary approach you expand your core expertise in computer science and applied mathematics, gaining the technical and analytical foundations needed to develop independent solutions to technical and scientific problems. The programme also emphasises the responsible application and critical evaluation of scientific results in real-world contexts.
The curriculum offers focussed tracks within information technology—such as machine learning, scalable software development and big data technologies—alongside applied mathematics. You can optionally select an area of application to tailor your studies. Project-based components ensure you encounter concrete use cases and help you build collaboration and communication skills alongside technical competence.
Practical training is central: you undertake a year-long software project where you practise data modelling and analysis, participate in at least one seminar to work intensively with relevant scientific literature, and complete an individual, research-related master’s thesis on a data science topic. Overall, the programme prepares you for technical and research-oriented roles that require strong data science expertise across industry and academia.
Key programme elements and requirements
The programme follows the Bologna framework: you are expected to earn 30 ECTS each semester, with a total of 120 ECTS across the Master's. The curriculum mixes advanced mathematics, computer-science electives, optional domain-specific study, hands-on project work and a research thesis so you build both theoretical foundations and practical data-science skills.
A dedicated set of compulsory elective mathematics modules (18 ECTS) lets you deepen applied-mathematics knowledge—giving you the quantitative tools to critically examine current research problems and apply modern analytical methods. A sizable block of free elective modules (24–48 ECTS) is taken from a broad list in computer science; you must select at least 24 ECTS here to tailor your technical profile and broaden expertise in algorithms, systems and data-methods. Optionally, you can pick an application area (18–24 ECTS) such as medical informatics, social sciences, geoinformatics or languages: these combine foundational courses from another discipline with data-science modules to prepare you for applying data techniques in a specific domain.
Practice-oriented and seminar modules (24–27 ECTS) train you in the scientific skills expected of data scientists: collaborative research projects that include modelling and implementation in substantial data-centric software, seminar work that develops critical evaluation of research, and an individual module on scientific working techniques. The programme concludes with a 30‑ECTS Master's thesis in which you carry out an independent research project under faculty supervision, applying the methods and competencies developed during coursework.
Curriculum components (ECTS)
Key learning outcomes
This consecutive Master's program builds directly on a relevant Bachelor's degree and expects applicants to already hold a solid academic foundation in related subjects. Eligible candidates will typically have completed a BSc in Data Science, Computer Science, Mathematics, or an equivalent university degree meeting the programme’s minimum GPA requirement. At the time you apply, you should have obtained at least 144 ECTS credits from your prior studies.
Applicants must be able to demonstrate substantial, subject-specific knowledge in both computer science and mathematics, with particular strength in machine learning. Admission decisions are made through an eligibility assessment procedure that evaluates whether your prior studies and competencies match the programme’s academic expectations. For complete, up-to-date information on formal requirements and the assessment process, consult the programme’s official admissions page.
Admission requirements (summary)
Further details and official guidance: https://www.uni-marburg.de/en/studying/degree-programs/sciences/datasciencems
Winter Semester (International)
All details can be found at:https://www.uni-marburg.de/en/studying/admissions/deadlines.
Graduates are prepared for technical and research-oriented roles in industry and academia that require expertise in data modelling, machine learning, scalable software systems and applied mathematics. Typical positions include data scientist, machine learning engineer, data engineer, analytics consultant or research assistant in sectors such as technology, healthcare, finance, geoinformatics and public institutions.
The programme’s project work (including a year-long software project) and research-focused master’s thesis strengthen employability by demonstrating practical implementation skills and the ability to tackle real-world, data-centric problems. The optional application-area modules further enable graduates to work in interdisciplinary teams and to apply data-science methods to domain-specific challenges; the degree also provides a solid foundation for pursuing a PhD in related fields.
Hochschule für Technik Stuttgart - University of Applied Sciences — Stuttgart
University of Regensburg — Regensburg
University of Bonn — Bonn
Brandenburg University of Technology Cottbus-Senftenberg — Cottbus