TU Wien Informatics

MSc Data Science UE 066 645

The English Master's program Data Science provides a scientifically and methodically founded education that is focused on lasting knowledge, enabling you to pursue both academic paths in subsequent doctoral studies and careers in a range of industry and business settings.

Facts

  • Duration: 4 Semesters
  • ECTS Worth: 120
  • Degree: Master of Science (MSc)
  • Language: English
  • Curriculum: PDF / List of Courses

Diese Seite auf Deutsch.

About

What are the contents of the program?

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 program conveys and integrates competences from the fields of information technology and mathematics as well as specific application disciplines—qualifications which 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 you can individually select thematically relevant courses.

Which qualifications do I acquire?

You receive extensive knowledge in a variety of topics such as mathematical foundations and methods of data science (in particular statistical data analysis and modelling), concepts and methods in specific informatics aspects of data science, in particular data infrastructures, data management, data analysis and visualization. You acquire 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).

Your cognitive skills are further developed and let you scientifically analyse systems, obtain an integrative view, and select the most suitable methods for modelling and abstraction. Your work methodology is goal-oriented, and you will be able to present results convincingly in an interdisciplinary environment.

Social skills such as self-organisation, personal responsibility, communication, and the reflection of your own abilities and limits are strengthened. You will be able to assess the impact of your results and to evaluate them on an ethical basis.

What can I do with my degree?

As a graduate, you have obtained an in-depth, scientifically and methodically founded education that is focused on lasting knowledge, enabling you to pursue both academic paths in subsequent doctoral studies and careers in a range of industry and business settings.

You 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. You are capable of deriving and understanding complex interrelationships, patterns and knowledge from raw data in a structured manner and to communicate the results.

You have the competencies to setup and operate data and computing centers, and are able to support research and innovation in the field of Science both from a core technical and an interdisciplinary perspective to drive the development of data-driven technologies.