Data Science (Master)

 Data Science - a short introduction

Joint Venture

The master's programmes Business Analytics, Data Science and Digital Humanities are a joint venture of the faculties of Business, Economics and Statistics, Computer Science, Historical and Cultural Studies, Mathematics, and Philological and Cultural Studies.

The master’s programme in Data Science aims at providing a practically oriented and scientifically sound education in the field of modern data science. Data science is an essential driving force in today’s digital world. In almost all areas of the economy, large amounts of data are collected and generated. Recently, data-driven methods have also found their way into various parts of the natural sciences and humanities. The task of data science is to gain knowledge from ever bigger volumes of data, which represents added value for the respective area. This requires not only the development of efficient algorithms, but also a basic understanding of the interpretability and reliability of results. A diverse and interdisciplinary competence profile is required, which particularly includes the practical handling of large amounts of data, a solid mathematical and statistical foundation as well as knowledge in the respective area of application. In addition, the rapid developments in data science raise ethical and legal questions. The master’s programme in Data Science at the University of Vienna extensively reflects all these core competencies and puts an emphasis on the interdisciplinary and heterogeneous character of Data Science, which is ensured by means of a specialisation in individual areas.

The degree programme requires knowledge of English language at level B2 (Common European Framework of Reference for Languages).

Further information on the degree programme is available here

Master of Science

Degree Programme Code: 066 645

4 semesters / 120 ECTS credits

Language: English

Special Admission Requirements


Learn more on "Data Science @ Uni Vienna"

Bild Data Science EN

Bild ist Dekoration/Decorative picture

Facts & Figures

  • Students: n.a.
  • Graduates in the last academic year: n.a.
  • Number of semesters needed for graduation (median): n.a.

Data updated on: 25.05.2020

*Click here for further information on statistical data in the field of teaching and learning. (in German)

Study Programme

The study programme consists of the compulsory module groups "Mathematical and Statistical foundations, Optimisation Methods, Machine Learning, Scalable Algorithms, Visual Data Analysis", "Doing Data Science", "Ethical and Legal Issues", "Specialisation in Areas of Data Science" and "Master’s thesis". In the module “Specialisation”, students can specialise in individual areas due to the multifaceted range of courses offered, e.g. in principles of Mathematics, Computer Science and Statistics or in various fields of application ranging from the Natural Sciences to Economics and from the Social Sciences to the Humanities. In order to complete the degree programme, students have to write a master’s thesis and pass a master’s examination.

As the degree programme is instructed in English, a proof of knowledge of English language at level B2 (Common European Framework of Reference for Languages) is required.

Focus on interdisciplinarity

The degree programmes Business Analytics, Data Science and Digital Humanities were designed together. Students of all three degree programmes complete the same course contents to the extent of one semester. The offered courses and the various disciplinary locations of students allow for a very broad interdisciplinary exchange.

Five Concepts

which you will deal with during your studies:

  • Mathematical & Statistical Foundations
  • Mining Massive Data
  • Optimisation
  • Visual & Exploratory Data Analysis
  • Ethics & Legal Issues

...and many more.

uniorientiert in a nutshell

Questions to the Answers of uniorientiert 2020


I do not have a computational background but would like to apply for the Data Science Master with my business background. Is it possible to do the necessary background in the first semester?
Can I hold a job in parallel to progressing through the programme?
Can I start in the summer semester instead of the fall semester?
Can I catch up with the necessary background?

 Overview of the programme structure & topics

Here you find the current offer of courses for this programme to gain better insight into the topics and structure. For more information please click on the respective level.

After Graduation

Graduates receive a sound and broad education in core modules, including algorithmic, mathematical and statistical foundations, the handling of large amounts of data as well as explorative data analysis. Additionally, graduates are familiarised with ethical and legal aspects.

They gain in-depth knowledge in concrete fields of application, e.g. from humanities, language processing, finance, medicine, physics, or computational science.

Thereby, they acquire the basis for a doctoral or PhD programme in Mathematics, Computer Science or Statistics/OR and practical skills which are in great demand on the labour market. For example, graduates are qualified to deal with huge volumes of data, statistically analyse complex data and develop, implement and analyse efficient algorithms for data analysis.

Graduates' Perspective on the Degree Programme

Graduate Survey

The University asks graduates to provide their opinion about the degree programme immediately after graduating. The survey results show how graduates evaluate the degree programme they completed from a subject-specific and organisational perspective.


The graduate survey is an important feedback tool for continuously improving the quality of studies.

Career Tracking of Graduates

The tracking of graduates provides information about the career paths of the University of Vienna graduates and aims at providing guidance for career entry after graduation (e.g. questions about the duration of job search until first employment after graduation, salary development and sectors that graduates are active in).