This two-year Master of Science is designed to train specialists in data science who want to apply quantitative methods to real-world social and business challenges. It is aimed primarily at students from business and social science backgrounds, but is equally suitable for those coming from health or environmental sciences who already have strong quantitative skills and want to work with big data, data-driven strategies, or computational social science.
Over the course of the programme you will build advanced technical and methodological expertise to analyse, model, forecast, and address complex problems encountered by digital societies, modern organisations, and competitive markets. The curriculum is interdisciplinary, drawing on courses from both social and data science disciplines, and includes three elective tracks that let you tailor your studies toward particular domains or career goals.
The degree places a strong emphasis on employability and prepares graduates for a wide range of professional and academic opportunities that emerge from digital transformation. It can also attract students from the humanities, natural sciences, or engineering who wish to focus on innovative social data analytics and the societal and business issues driven by digital change.
Key facts and candidate profile
Program overview The programme brings together social sciences, business studies and data science into a single, interdisciplinary curriculum. Students can draw on courses across those areas and collaborate with faculty and peers in related departments such as computer science, environmental science and the life sciences. The structure deliberately supports individualisation, so you can shape your studies around your interests and career goals by choosing one of three elective tracks.
Tracks and key modules
Learning outcomes Graduates will be able to apply computational and statistical methods to real-world social, business, health and environmental problems; design and evaluate models for domains such as urban systems and supply chains; and integrate domain-specific knowledge from natural and social sciences with data-driven techniques. The programme’s elective-driven format and cross-department collaboration help you specialise in a niche area while building transferable analytical and interdisciplinary research skills.
Concise requirements / choices
The admissions committee requires a set of documents that demonstrate your academic background, motivation, and language ability. Make sure each file clearly shows your qualifications and readiness for the interdisciplinary Data Science program.
Important notes: transcripts must be provided in English or German. If you have not yet received your final bachelor's certificate, you may submit it later. Check the program’s information for exact English proficiency requirements and accepted test formats.
Winter Semester (International)
1 June 2026
Winter Semester (EU/EEA)
31 July 2026
Graduates are prepared for roles that combine domain knowledge with data-science expertise. Typical career paths include data scientist, data analyst, business intelligence specialist, policy or social-data analyst, health/environmental data consultant, and roles in consulting, supply chain or finance where data-driven decision-making is critical. The interdisciplinary training also provides a foundation for academic research or PhD study in computational social science and related fields.
The programme’s emphasis on applied methods, internships and cross-disciplinary collaboration helps students build a professional portfolio attractive to employers in technology, healthcare, public sector, environmental organisations, consulting firms and industry research labs.
Trier University of Applied Sciences — Birkenfeld
Technische Universität Braunschweig — Braunschweig
Furtwangen University — Villingen-Schwenningen
University of Siegen — Siegen