Quantitative Data Science Methods â€“ Psychometrics, Econometrics and Machine Learning (QDS) at University of Tübingen

About

The QDS Masterâ€™s programme encourages a focus on research andÂ the development of methods. It expands and deepens methodological and technical knowledge, enables graduates to carry out academic research, provides the basis for advancing the field, and prepares graduates for subsequent PhD studies. The QDS Masterâ€™s programme is a methodological programme that covers a wide range of topics, from fundamental skills in statistics and data handling to advanced methods of modern data analysis using a variety of methods. Graduates are not only able to apply these methodsÂ but also to evaluate and develop them as well as quickly take up new research developments in the three areas of interest (Psychometrics, Econometrics and Machine Learning). Through optional specialisations, further expertise in relevant areas can be gained. Foundations This area covers general statistical and technical modules. Depending on the prerequisites that have been covered in a student's previous degree programme, this area provides an opportunity to catch up. For this purpose, personalised module combinations can be offered, focusing on topics such as statistics and probability theory or on techniques such as programming. In QDS-FO, at least 12 ECTS points have to be earned. 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 response 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 of study within this area is flexible, but either Advanced Time Series Analysis or Advanced Microeconometrics have to be attended. Machine Learning The area of Machine Learning introduces key concepts of the field. 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 isÂ 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.

Requirements

Entry Requirements

Bachelorâ€™s degree or equivalent in a field that includes a mathematical or statistical focus (mathematics, data/computer science, physics, economics, quantitative psychology, and related fields) with an overall grade better than 2.5 (This refers to the German system where 1.0 is the highest. For queries concerning the equivalence of non-German qualifications, please contact the programme adviser.) Applications must include evidence of proficiency in the following fields: one- and multi-dimensional calculus, linear algebra, and either statistics or probability theory. Strong background in mathematics, statistics, and probability theory Basic/first knowledge in programming, algorithms, and data structure is required.