Informatik, TU Wien

Master program Data Science


Data Science deals with large, heterogeneous data (big data) from various application areas (e.g. production, energy, environment, health, social sciences) and aims to obtain valuable and meaningful findings and generate actionable information.
Tasks in Data Science include obtaining a thorough understanding of the problem domain (Business Understanding), processing and fusion of heterogeneous data from different sources (Data Gathering), analysis and statistical modelling of the Data (Data Analytics), as well as interactive visualization of the data (Visual Analytics) and the use of the results (Decision Support and Deployment).
Furthermore, requirements in terms of reproducibility of results and reuse of data (Data Curation) and the deployment within large data centers are of central importance.
This curriculum conveys and integrates competences from the fields of information technology and mathematics as well as specific application disciplines. These qualifications are increasingly demanded in science and business.

The curriculum builds upon a few central foundational subjects which are extended by selecting at least three of the following four Key Areas:

  • Fundamentals of Data Science
  • Machine Learning and Statistics
  • Visual Analytics and Semantic Technologies
  • Big Data and High Performance Computing

Each key area consists of a mandatory "gatekeeper" module (core module) and an extension module, from which thematically relevant courses can be freely selected; if necessary, additional courses may be required as condition for admission to the masters program.


Professional Activity

The Master's program provides an in-depth, scientifically and methodically founded education that is focused on lasting knowledge, enabling graduates to pursue both academic careers in subsequent doctoral studies as well as to be competitive in a range of industry and business settings.
Graduates are:

  • qualified to act as a link between the technical infrastructures and the domains in research and development in industries such as pharmaceutics, operations research, nanotechnology, marketing, logistics.
  • capable of deriving and understanding complex interrelationships, patterns and knowledge from raw data in a structured manner and to communicate the results.
  • competent in contributing to the setup and operation of data- and computing centers.
  • able to support research and innovation in the field of eScience both from a core technical as well as an interdisciplinary perspective to drive the development of data-driven technologies.


Due to the occupational requirements, qualifications regarding the following categories are included in the Master of Data Science:

Subject-specific skills

  • In-depth mathematical foundations and methods of data science (in particular statistical data analysis and modelling)
  • In-depth concepts and methods in specific informatics aspects of data science, in particular data infrastructures, data management, data analysis and visualization
  • Solid basics and methods in selected areas of other scientific disciplines (such as architecture, astronomy, biology, chemistry, digital humanities, earth sciences, medicine, physics, social sciences)


Cognitive skills

  • Scientifically based system analysis
  • Integrative view
  • Selection of suitable scientific methods for modelling and abstraction
  • Solution finding and evaluation
  • Comprehensive and precise documentation of solutions and their critical evaluation
  • Ability to present convincingly in an interdisciplinary environment
  • Goal-Oriented Work Methodology


Social skills

  • Self-organisation, initiative and personal responsibility
  • Increase of individual creativity and innovation potential
  • Problem formulation and problem solving competence
  • Communication and ability deal with criticism
  • Reflection of one's own abilities and limits
  • Competence for teamwork and responsibility in complex projects interdisciplinary projects
  • Impact assessment and ethical evaluation
  • Strategic thinking and planning


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