Overview This English-language master's combines classical quantitative methods (statistics, econometrics, optimisation, simulation) with contemporary data-science techniques (machine learning and artificial intelligence) using R and Python. It integrates these analytical tools with up-to-date lessons in economics, management and entrepreneurship so you learn to apply analytics directly to business and economic problems. The course balances solid theoretical foundations with practical relevance, preparing you for careers that require rigorous data-driven decision making in a digital environment.
Capstone and focus areas A one-semester Capstone Project brings students together with corporate or public partners to tackle real-world business, economic or societal challenges in teams, applying cutting-edge analytics to produce actionable results. The curriculum emphasizes modern digital topics (big data, digital innovation and transformation, digital business strategy, digital entrepreneurship), advanced methods (econometrics, Bayesian analysis, machine learning/AI), and the ethical, privacy and security issues that arise from digitalisation. Graduates leave able to collect, manage and analyse (big) data, visualise and communicate insights, and pursue data-analytics roles or digital ventures.
What you will gain (key outcomes)
Taught by an internationally recognised faculty with expertise in business administration, econometrics and information systems, this programme builds a strong foundation in data-driven decision making and digital transformation. In the first year you cover core modules that introduce the foundations of data analytics, principles of digitalisation, and applied data analytics techniques — equipping you with the statistical, computational and conceptual tools needed to work with data in organisations. Faculty members are available to support your learning both inside and outside scheduled classes.
In the third semester you choose one of two specialised tracks. The Econometrics Track emphasises rigorous statistical and analytical methods for identifying and solving complex economic problems across private, public and non‑profit sectors, blending classical econometric approaches with modern data-analytic techniques. The Business Analytics Track focuses on applying analytics methods to real-world business challenges with (big) data, stressing how to create a data-driven digital strategy, develop data-driven business models, pursue digital entrepreneurship and — for those interested — the practical skills needed to found a start‑up.
Graduates leave able to design and implement analytical solutions, interpret and communicate econometric and data-analytic results, and translate insights into strategic business or policy decisions. The programme prepares you for roles in consultancy, corporate analytics, public sector analysis, and entrepreneurial ventures, benefiting from hands-on instruction and faculty mentorship throughout the course.
Requirements / programme structure (key points)
This master’s program requires a solid overall Bachelor’s performance plus specific prior coursework in economics/business and quantitative subjects. International applicants should prepare transcripts that clearly show ECTS credits and a grade conversion or explanation if their degree uses a different grading scheme. ECTS are the European Credit Transfer System; 30 ECTS typically represents about half an academic year of study, so the programme expects substantive prior exposure to the listed fields.
The minimum academic grade is expressed as a German grade (2.5). If your diploma uses another scale, provide documentation or a conversion so the admissions office can assess equivalence. Make sure your transcript(s) explicitly list course subjects and credit values so your completed ECTS in Business/Economics and in Statistics/Mathematics/Information Systems/Informatics can be verified.
Winter Semester (International)
15 June 2026
Winter Semester (EU/EEA)
15 June 2026
Graduates are prepared for analytical and strategic roles in business, government and non-profit sectors. Typical positions include data scientist, business analyst, econometrician, digital strategist, and consultant across industries such as finance, technology, consulting, public policy and healthcare.
The programme also equips students with entrepreneurial skills to found or join start-ups and to design data-driven business models. Employers will value the combination of rigorous quantitative training, practical tool experience (R/Python), and experience working on client-based Capstone Projects.