Master of Science in Statistics – Joint Master's Programme

The Master of Science in Statistics programme embraces the increasingly interdisciplinary nature of the subject and is composed of a consortium of faculty and universities including: Humboldt-Universität zu Berlin (School of Business and Economics and Department of Mathematics), the Freie Universität Berlin, the Technische Universität Berlin and the Charité-Universitätsmedizin Berlin. The programme is designed for students with a university degree (Bachelor's, Master's or diploma) in a quantitative subject. Compulsory Area  The compulsory area is intended to establish a solid mathematical foundation in probability theory and mathematical statistics. Specialisation Area Two out of the seven areas of specialisations below need to be chosen: Statistical Inference Econometrics Quantitative Methods of Financial Markets Survey Statistics Applied Microeconometrics and Economic Research Statistics in the Life Sciences Data Science Interdisciplinary Area  The interdisciplinary area accounts for general studies aspects such as language courses, interdisciplinary and professional courses from abroad, internships and much more. The Master of Science in Statistics is an accredited consecutive graduate programme comprising 120 ECTS credits and typically completed over four semesters. Compulsory Area (62 CP) Students complete core modules including Stochastic (10 CP) Econometric Methods or Statistical Methods (10 CP) Multivariate Statistical Analysis (6 CP) Advanced Econometrics or Advanced Statistics (6 CP) and Master’s thesis (30 CP). Interdisciplinary Area (10 CP) Specialisation Area (48 CP) Students choose two out of seven specialisation tracks. Detailed course information is provided in the official module descriptions. Statistical Inference This focuses on modern statistical methods to infer population properties from data. Students learn to formulate problems, compute solutions, and apply methods in research and projects, with flexible training in theory and application. Econometrics This covers statistical tools to analyse economic data. It emphasises microeconometrics, panel data, treatment effects, and time series. Students apply methods in projects, assess current research, and conduct their own research. Quantitative Methods of Financial Markets This teaches statistical and econometric tools for financial market analysis. Topics include option pricing, ARIMA, GARCH, point processes, and risk models like VaR. It prepares students for finance and insurance roles. Survey Statistics This focuses on analysing population data and covers survey methods like calibration, weighting, small sample estimation, missing data, and panel analysis. It prepares students for roles in official statistics. Applied Microeconometrics and Quantitative Economic Research This introduces methods for analysing economic data using statistics and machine learning. The emphasis is on decision-making, treatment effects, and practical application with programming. It enables independent project work. Statistics in the Life Sciences This covers Biometrics and Psychometrics with applications in medicine and psychology. Includes methods for censored/dependent data, small samples, clinical study design, and multiple testing. It prepares students for roles in biomedical or pharmaceutical research. Data Science This equips students with statistical and machine learning skills for data analysis, visualisation, and predictive modelling. It focuses on real-world applications, optimisation, and project-based learning for data-driven solutions. PDF Download