This English‑taught Master's programme equips you for roles such as data scientist, AI specialist, data engineer or data analyst and opens pathways into sectors like biomedical research, technology, business and other forward‑looking fields. The course emphasises interdisciplinary problem solving and trains you to design innovative, data‑driven solutions by deepening your understanding of statistics, machine learning and their practical applications.
The curriculum is built from three main parts: a compulsory area (including the Master's thesis), a compulsory‑elective area focused on Machine Learning and Statistics, and a compulsory‑elective Specialisation area. Early in the programme you take the lecture "Modern Machine Learning" with an associated lab (6 ECTS) to ensure familiarity with current deep learning and reinforcement learning methods. The compulsory area also includes two elective modules (12 ECTS each) that you can choose from computer/data science or other disciplines, a seminar on current data science topics (6 ECTS) and the Master's thesis (30 ECTS).
In the Machine Learning and Statistics elective area you must select at least 12 ECTS from a planned set of 11 modules that cover core data science topics. Example modules include Statistical Machine Learning, Advanced Statistics I & II, Advanced Explainable AI, Advanced Data Engineering and Digital Image Processing – AI‑based approaches, allowing you to concentrate on central methodological skills.
You are required to choose one of four specialisations and earn at least 42 ECTS within that track. The available specialisations are Machine Learning and Statistics, Computational Life Sciences, Human‑Centred Data Science and Information Systems. Each specialisation includes its own compulsory‑elective options so you can tailor your studies to your interests while meeting the mandatory requirements.
Program requirements / degree structure (key facts)
This master's curriculum trains you to become an expert at the intersection of statistics, computer science and applied domains. The programme combines taught modules (including core topics such as Modern Machine Learning, Current Topics in Data Science, a Free Elective and Studium Universale) with hands-on laboratory work, seminars and project-based courses so you learn both the theory and how to apply methods yourself. Across the degree you will develop practical data-science skills, the ability to design and run research projects, and the communication skills needed to present and critically evaluate scientific work.
Teaching is delivered through lectures with accompanying exercises and labs, seminars, project seminars, practicums and possible internships. Labs and coursework let you practise methods introduced in lectures, while examinations (written or oral) assess both knowledge and method application. Seminars train you to investigate current research independently, produce scientific texts and deliver formal presentations (typically assessed by a written paper and a presentation). Project seminars and practicums—often team-based—give authentic research experience and require a project presentation and a project paper or documentation as the assessed output, preparing you for the Master’s thesis.
The programme is organised so you take required courses, machine‑learning/statistics electives and your chosen specialisation during the first three semesters, and then concentrate on your Master’s thesis in the fourth semester. If you complete at least 42 ECTS within one specialisation and write your thesis on a topic from that specialisation, the specialisation can be recorded on your degree certificate.
Program requirements and structure (summary)
This master's program expects a solid quantitative background and proof of English ability. Applicants should hold a recognised Bachelor's degree with a sufficiently good final grade; if your undergraduate degree is not in data science or computer science, you can still be considered provided you meet the subject-specific credit and language requirements. Some applicants may be admitted conditionally and asked to make up missing coursework.
If your first degree was obtained outside countries that signed the Lisbon Recognition Convention, you must also take the GRE General Test to demonstrate academic readiness. Full information on application deadlines, required documents and procedural details is available on the program’s website — check there before you apply.
Required items and criteria (bullet points)
For up-to-date deadlines, application steps and the full list of required documents, consult the program’s official website before applying.
Winter Semester (International)
1 May 2026
Summer Semester (International)
1 November 2026
Winter Semester (EU/EEA)
1 June 2026
Summer Semester (EU/EEA)
1 December 2026
Graduates leave prepared for technical and interdisciplinary roles such as data scientist, AI specialist, data engineer or data analyst across sectors including biomedical research, industry and business. The programme’s mix of advanced theory, hands-on labs and project work equips you to develop, evaluate and deploy data-driven solutions and to pursue further research.
With the option to specialise and a substantial Master's thesis, alumni are well positioned to enter R&D roles or continue into doctoral studies. The practical project experience and applied coursework also suit graduates targeting industry positions that require immediate technical contributions in machine learning, statistics and data engineering.
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