Large-scale data have become ubiquitous. In the social and behavioral sciences, including psychology and economics, research increasingly depends on the appropriate handling of such data using quantitative methods. However, experts who are able not only to apply these methods but also to develop them and critically reflect on their use remain scarce.

At the same time, recent advances in machine learning and large language models have substantially expanded the range of analyzable data. In particular, new forms of unstructured data (e.g., text, images, and interaction data) open up novel opportunities for research in the behavioral and social sciences, while also increasing methodological and conceptual demands.

The University of Tübingen has a strong research profile in all three core areas. Top-level researchers from all major methodological branches of Quantitative Data Science (QDS) will actively contribute to teaching on the program.This includes members of the Methods Center, the School of Business and Economics, the working group on Research Methods and Mathematical  Psychology (Department of Psychology) and Machine Learning experts of the Department of Computer Science.

The program is aimed at students with a strong background in mathematics, the natural sciences, or engineering with a strong interest in applying quantitative methods to the behavioral and social sciences. It is equally suited for students of psychology and economics who can demonstrate solid training in mathematics and statistics (e.g., linear algebra, calculus).

The ideal student is motivated by interdisciplinary inquiry and seeks to develop an integrative perspective on data analysis across disciplinary boundaries. The program is designed for students who wish to combine methodological rigor with substantive questions from the behavioral and social sciences, rather than focusing exclusively on programming skills or specializing solely in a single discipline (e.g., machine learning).

A self-assessment tool provided by the Methods Center can be used to guide the applicants to decide if their skill set fits to the program content.