• English

You can complete the programme entirely in English.

Winter semester: 31 May
Summer semester: 30 November

Integrated research and degree programmes designed for the future: The TUM School of Computation, Information and Technology (CIT) at the Technical University of Munich (TUM) unites the disciplines of Mathematics, Informatics, and Electrical and Computer Engineering. Our research and teaching activities range from theoretical knowledge to its application, focussing across disciplines on the major challenges of our time, such as the digital transformation. At the same time, our expertise remains firmly embedded in our core disciplines.

Handling and analysing very large amounts of data is an urgent problem in many areas of science and industry, one that requires novel approaches and techniques. The trend towards "big data" is caused by a host of developments:

1. The creation and storage of large data sets becomes feasible and economically viable, for example, due to price decreases in storage space, sensors, smart devices, social networks, and other factors.
2. Technical advances, for example, in multi-core systems and cloud computing, make it possible to examine data sets on a large scale.
3. Such amounts of data not only have their origin in the "classical" domains like business data, but are now created in many areas of life. Consider vehicles that create sensor data and share information via intelligent networking, or consider data that is created by intelligent energy grids.

The Master's programme in Data Engineering and Analytics steps up to these developments and provides an education that on the one hand enables graduates to design and plan industry grade solutions in the area of Big Data and on the other hand creates a solid starting point for ventures into research.

The programme is divided into three areas of study: Data Analysis, Data Engineering and Data Engineering and Analytics:

• Data Analysis is concerned with the fundamentals of understanding and modelling data and the underlying relationships therein. It is also concerned with topics that require solid mathematical foundations, including the following: Fundamentals of Convex Optimisation, Computational Statistics, and more.
• Data Engineering consists of lectures about the construction of systems that perform efficient and scalable data processing, thus enabling methods of data analysis on large data sets. This area of study also contains lectures about distributed systems, distributed databases, query optimisation, database systems on modern CPU architectures, and high performance computing. The curriculum comprises mandatory courses on Data Analysis and Data Engineering.
• Data Engineering and Analytics offers lectures about machine learning, business analytics, computer vision, and scientific visualisation.

A Master's degree in Data Engineering and Analytics from TUM will enable you to work in executive positions in industry and will qualify you for a career in research (PhD).

More information here: https://www.cit.tum.de/en/cit/studies/degree-programs/master-data-engineering-and-analytics/