Quantitative Data Science Methods – Psychometrics, Econometrics and Machine Learning (QDS) Quantitative Data Science Methods (QDS)

The QDS Master’s programme promotes a focus on research and methods development. It expands and deepens methodological and technical knowledge, enables graduates to work scientifically, provides the basis for advancing the field, and prepares graduates for subsequent PhD studies. The programme specifically empowers graduates to take up responsible leading roles and emphasises a scientific, research-oriented mindset based on independent thought, judgement, and decision-making. The QDS Master’s programme is a broad-based methodological programme. Graduates are not only able to apply methods but also able to evaluate and develop methods in the three areas of interest. Through the respective specialisations further expertise in relevant areas is gained. There is a strong cooperation between research institutes within and outside the university. The programme offers first-class teaching, and state-of-the-art applications are taught. Foundations This area covers general statistical and technical modules. Depending on the individual's prerequisites from the qualification degree, this area can serve to compensate for heterogeneity. For this purpose, personalised module combinations can be offered, focusing for example on statistics and probability theory or techniques such as programming. It is recommended to cover this area within the first two semesters of the programme. Psychometrics In psychometrics and mathematical psychology, students learn about typical methods used in these fields, such as (semiparametric) latent variable modelling, item modelling, dynamic longitudinal modelling, Bayesian statistics, knowledge space theory, models for decision-making, etc. Students learn to reflect critically on any problematic assumptions of the methods and to know their limitations. Econometrics In this area, quantitative methods used in econometrics are introduced. The programme within this area is flexible and methods such as time series analysis and machine learning are taught to be applied to topics like microeconometrics or financial markets. Machine Learning The area of machine learning introduces key concepts of the field such as data literacy, deep learning, and statistical and probabilistic machine learning. Data Ethics The increasing use of data and data driven applications affects our daily lives, for example, in decision-making processes. Thus, ethical discussion on the responsible usage of data is of growing importance. Through appropriate supplementary events and a varied programme of seminars, graduates will be able to reflect the ethical and moral handling of current topics of data science. Project Seminar The project seminar will involve each student undertaking his or her own research project. This project serves to deepen theoretical and practical knowledge in a specific field and can be carried out in any of the core disciplines. The topic of the research project can be included in optional areas of specialisation. The project seminar can be completed as a group. The topic can be researched in conjunction with the research groups at the university.