This English-taught Master of Science is a research-informed, hands-on programme that trains you to analyse and address complex social and economic problems using modern data techniques. You will learn to combine advanced analytical tools with social science perspectives to generate insights into real-world challenges.
The curriculum builds firm foundations in data science, social science theory, and methodological skills, and takes an interdisciplinary approach to topics such as economic behaviour, social dynamics, and political processes using contemporary data-analytic methods. Through coursework, research projects and collaborative activities you will develop critical thinking, the ability to work across disciplinary boundaries, and the capacity to turn data into meaningful conclusions. Graduates leave prepared for careers in academia, policy-making, or data-driven private and public sectors.
This MSc is especially well suited to students from social and behavioural sciences who want to deepen their quantitative and computational abilities. It emphasizes both theoretical understanding and practical application, so applicants should be ready to engage with rigorous empirical and methodological work.
Requirements / key facts
This master’s curriculum begins with two semesters that establish the technical and methodological groundwork needed to analyse social and economic data. In the first semester students develop programming literacy in Python and R (Tidyverse), and cover core mathematical and statistical ideas through the introductory Computing for the Social Sciences module (both theory and practice). The second semester advances those foundations into deeper statistical and computational methods—topics include statistical modelling and inference, social network analysis, statistical learning and machine learning, alongside computer science subjects such as databases, algorithms, big‑data management and data visualisation.
The third semester shifts to applied, project‑based work. Through the Social Science Applications module, students choose between seminar options or a Data Science Project (DSP) carried out in academia, industry or public institutions, taking a project from concept to (software) implementation. External collaborations with companies, government agencies or NGOs are actively encouraged, and students may also complete their DSP abroad. A Supplementary module lets students broaden their profile with internships, summer schools, language courses, hackathons or additional programming.
The final semester is devoted to independent research: students produce a Master’s thesis that demonstrates the ability to design and execute a substantial scientific project using social and economic data‑science methods. A colloquium accompanies the thesis period, providing a forum to present, defend and discuss research design and findings.
Key modules and learning outcomes
You will need an above‑average Bachelor’s degree — at minimum the equivalent of the German grade 2.5 (classified as “good”). Suitable academic backgrounds include disciplines that provide quantitative or social‑science foundations, for example: Political Science, Psychology, Sociology, Economics, Computer Science, Mathematics, or Statistics.
You may apply while you are still in the final year of your Bachelor’s programme. In that case, submit your current academic transcript as a PDF. If your documents are not in English or German, provide an officially certified translation into one of those languages. For full details on eligibility checks, grade conversion and the online application steps, consult the programme’s admissions webpage.
Requirements (concise)
Winter Semester (International)
30 April 2026
Winter Semester (EU/EEA)
30 April 2026
Graduates are prepared for roles that require both social-scientific insight and advanced quantitative skills. Typical career paths include positions in academia and research (e.g., research associate), public policy and government (e.g., policy analyst, evidence-based policy adviser), and data-driven private sector roles (e.g., data scientist, data analyst, analytics consultant).
The programme’s emphasis on applied projects, internships and external collaborations helps students build a professional portfolio and practical experience valued by employers in industry, NGOs and public institutions.