The Centre for Social Data Science (CSDS) advocates the use of data science methods and data-driven inquiry within social sciences.

CSDS promotes open data, open science and data-driven research inquiry. CSDS develops and maintains infrastructures for data collection and analysis. In addition, CSDS aims at increasing the collaboration between researchers and methodology experts, with the goal of improving both applied research as well as basic research on research methods.

CSDS is the home base of the discipline of Social Data Science (formerly called Social Statistics). Social Data Science supports evidence-based decision making for the government, organizations and private companies. Applications range from pensions and health care to finance and social media. Statistical analysis is used to explore various topics, such as questions of inequality between and within genders, regions or generations. Dynamic probabilistic modelling is a primary tool in analyses involving forecasting and risk management. Machine learning methods can be used quantitative data, but also on textual, audio and visual materials. We examine various data sources: surveys, interviews, register data, open governmental and business data, big data and digital trace data. However, the essential focus in Social Data Science is to ground the data analysis in social science through its theories and concepts.

CSDS is part of the Faculty of Social Sciences.