This Master’s programme trains you to manage the growing digitisation in the physical and life sciences by giving you the tools to collect, process, analyse and interpret large digital datasets. Teaching focuses on the core elements of modern data science: a mix of mathematics, statistics, computer science and machine learning, all taught with attention to real-world applications. You will learn to identify data-analysis problems in specific scientific domains, design or adapt mathematical and computational solutions, and correctly interpret results in their application context.
The course offers substantial, subject-specific depth in mathematics, statistics and computer science while also covering the application areas within the physical and life sciences that rely on quantitative methods. Two profile tracks let you tailor your studies: the Life Sciences track, for applying data-science methods to biological, biomedical and health-related topics; and the Data Science Technologies track, for deepening computational and algorithmic expertise with an emphasis on software, AI and big-data infrastructure.
Key points for international applicants
Admissions note
This 120-credit Master’s programme combines taught coursework (90 credits) with a research-based Master’s thesis and colloquium (30 credits). The taught portion is split into a 30‑credit fundamental area, normally taken in the first semester, and a 60‑credit profile area completed across the second and third semesters. The Master’s thesis is typically undertaken in the fourth semester after students have finished at least 60 credits; thesis work has a 23‑week processing period during which students present progress and defend the work in an accompanying colloquium.
The fundamental area provides the core technical foundation and introduces the programme’s specialization options. Required modules are: Introduction to Profile Areas; Statistics for Data Science; Machine Learning for Data Science; and Programming for Data Science. Learning outcomes from this phase include solid grounding in statistical methods, machine learning concepts and algorithms, and practical programming skills needed to handle and analyse real-world data, plus an awareness of the available specialization tracks.
In semesters two and three students choose one of two profiles — Data Science in the Life Sciences or Data Science Technologies — to develop domain-specific expertise. Both profiles include a mandatory module, Ethical Foundations of Data Science, and require a 15‑credit elective area where students can select complementary courses from the other profile or from other programmes. Expected outcomes of the profile phase are the ability to apply data‑science methods to a chosen domain, critically evaluate ethical issues in data work, and design and execute a substantive independent research project in the thesis semester.
Requirements (concise)
For full details on course offerings, assessment rules and registration, consult the programme website and the study regulations.
This master’s programme requires a relevant first university degree and a solid foundation in computer science and mathematics. You may apply if you have already completed a Bachelor’s (or equivalent) or if you are in the final phase of your undergraduate studies. The degree must come from a programme of at least six semesters (or 180 ECTS) in Germany or be judged equivalent from abroad.
Applicants with a Bachelor of Science in computer science meet the subject requirement directly. If your bachelor’s is in another field, you must demonstrate specific coursework in mathematics and computer science (details below). If your degree was earned outside Germany, your application must be submitted via uni-assist for document verification and forwarding. Please review the programme homepage and FAQ for full application instructions, and contact the Student Services Centre for any questions about application, admission, enrolment or orientation.
Admission requirements (bullet points)
Winter Semester (International)
31 May 2026
Winter Semester (EU/EEA)
31 May 2026
Graduates will be prepared for roles that require advanced skills in data collection, processing, analysis and interpretation across sectors that rely on quantitative methods. Typical positions include data scientist, machine learning engineer, and data analyst in industry, research institutions, and public sector organisations, with particular applicability to physical and life science domains.
The programme’s two specialisations expand opportunities: the Life Sciences track targets biomedical, health and biological application areas (e.g., bioinformatics, health data analytics), while the Technologies track readies graduates for technical roles in software engineering, AI development and big data infrastructure. The practical modules, internship option and substantial thesis work support employability in both applied and research-oriented careers.
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