Applied Data Science (MSc)

This master’s programme trains you to turn data into actionable knowledge by combining mathematical, statistical and computer‑science methods with expertise from a specific application area. The curriculum emphasizes interdisciplinarity and prepares you to apply core techniques from these fields to real problems. You select one application domain—options currently include Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities, Computational Sustainability, and Digital Business Administration—with Physics planned to be added in the near future.

The degree is research‑oriented: you will gain the skills needed to design and carry out independent scientific research projects and to present your results clearly. Ethical issues and the societal impacts of collecting and analysing large datasets — including consequences of automated, data‑driven decision‑making — are an explicit part of the programme. In addition to research training, there are opportunities for internships and collaboration with industry partners to broaden practical experience and career prospects.

Key facts and applicant expectations

• Instruction language: English.
• Strong emphasis on interdisciplinary work linking maths, statistics and computer science to an applied domain.
• Current application domains: Computational Neuroscience; Bioinformatics; Medical Data Science; Digital Humanities; Computational Sustainability; Digital Business Administration. Physics will be added.
• Programme goal: enable autonomous scientific research and effective communication of results.
• Ethical reflection on data collection, analysis and automated decision‑making is integrated into the curriculum.
• Opportunities for internships and industry engagement.

Recommended background (skills that will help you succeed)

• Solid foundations in mathematics, statistics or computer science (or closely related areas).
• Interest in applying quantitative methods to a chosen domain.
• Motivation for research and independent project work.

Curriculum overview

This 120 ECTS master’s programme is structured into three integrated parts: a core curriculum, an area of professionalisation, and a master’s thesis. The core courses build a rigorous, cross-disciplinary foundation in data science by combining computer science, mathematics and statistics with attention to ethical issues arising from data-driven work. The professionalisation segment lets you specialise in an application domain, gain hands‑on experience through a research lab rotation or an industry internship, and develop additional practical and transferable skills. The programme culminates in an individually supervised master’s thesis that synthesises methods and domain knowledge.

Core modules focus on the mathematical, statistical and algorithmic pillars of modern data science and on responsible practice. Key taught modules include Fundamentals of Data Science, Statistical Methods of Data Science, Computer Science Methods of Data Science, and Machine Learning Methods, alongside coursework addressing ethical aspects of data use. In electives and professionalisation activities you deepen technical abilities and apply them to real problems.

The professionalisation area is highly flexible so you can align studies with your career goals. You select one application domain—options include Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities, Computational Sustainability, and Digital Business Administration—then complement it with elective data science courses and a research lab rotation or industry internship to gain either research experience or workplace exposure. Graduates leave with strong methodological skills, domain-specific expertise, practical project experience and professional competencies that support roles in industry, research labs or interdisciplinary teams.

• Total requirement: 120 ECTS (European Credit Transfer and Accumulation System)
• Three main components: core curriculum, area of professionalisation, master’s thesis
• Core modules: Fundamentals of Data Science; Statistical Methods of Data Science; Computer Science Methods of Data Science; Machine Learning Methods; and courses on ethical aspects of data science
• Professionalisation: choose one application domain (Computational Neuroscience; Bioinformatics; Medical Data Science; Digital Humanities; Computational Sustainability; Digital Business Administration)
• Practical experience: research lab rotation or industry internship
• Additional elements: elective data science courses and targeted key‑competency training
• Expected outcomes: strong cross‑disciplinary methodological foundation, domain specialisation, applied research or industry experience, and professional/ethical competence for data‑driven roles

Graduates are prepared for data science roles in academia, research institutes and industry, especially in domain-specific settings corresponding to their chosen specialisation (e.g., healthcare, sustainability, digital businesses or humanities research). The combination of method-focused training, applied specialisation and practical experiences such as internships increases employability for positions like data scientist, machine learning engineer, bioinformatics analyst or research associate.

The programme also provides a solid foundation for further academic work (PhD) for students interested in pursuing research careers. Industry links and internship opportunities help students build professional networks and gain practical experience relevant to employer needs.