Quantitative Data Science Methods Master's Programme
A research-oriented Master's programme bridging psychometrics, econometrics, and machine learning.
Best for:Best for analytical graduates in mathematics, computer science, or quantitative fields seeking advanced methodological training in machine learning, psychometrics, and econometrics.
Overview
The Quantitative Data Science Methods (QDS) Master's programme at the University of Tübingen is an interdisciplinary, four-semester course of study designed for students pursuing rigorous methodological training. Developed in cooperation with the Cluster of Excellence "Machine Learning in Science," the curriculum equips graduates to evaluate, develop, and apply advanced quantitative techniques across psychometrics, econometrics, and machine learning. Students complete interdisciplinary foundations alongside specialized coursework, a research project, and a Master's thesis. The programme prepares candidates for leading professional roles or subsequent doctoral studies.
What you'll do
- Complete rigorous coursework across interdisciplinary foundations, psychometrics, econometrics, and machine learning modules
- Earn a minimum number of ECTS points across diverse methodological domains as outlined by the curriculum
- Conduct an independent research project applying quantitative methods to complex data problems
- Write and defend a Master's thesis focusing on a specialized area within quantitative data science
- Engage with interdisciplinary seminars including topics on ethics in technology, data science, and artificial intelligence
Requirements
Degree required: Bachelor's degree in Mathematics, Computer Science, Physics, Economics, Quantitative Psychology, or a related field with a grade of at least 2.5
Degree offered: Master of Science
Language: English; B2 proficiency demonstrated via Cambridge C1 Advanced, TOEFL iBT 79, IELTS Academic 6.5, or an equivalent qualification
Instruction: English
- Prior knowledge in mathematics, methods, or programming covering calculus, linear algebra, and probability theory
- Basic competency in algorithms and data structures using languages such as R or Python
- Foundational understanding of empirical work in social and behavioural sciences including latent variable concepts
- Strong academic record demonstrated through university transcripts and prior coursework
- Open to applicants of all nationalities meeting the academic and language prerequisites
Funding & terms
Programme and admissions
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How to apply
Deadline: 2026-05-31
Applicants must submit their application materials through the official University of Tübingen online admissions portal before the specified deadline. The application package requires academic transcripts, module handbooks detailing course descriptions, a curriculum vitae, and an English language certificate. A letter of motivation and interview performance are also considered during the holistic review process.
- CV
- Bachelor's degree transcript
- Module handbook course descriptions
- English language certificate
- Letter of motivation
Contact
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