Data Engineering and Analytics (MSc)
Technical University of Munich · Munich, Germany
- Level
- Master's
- Award
- Master of Science
- Language
- English
- Duration
- 4 semesters
- Campus
- Garching b. München
- Apply by
- Register by 30 November 2026 + 1 more
- Starts
- Winter and summer semester
- Tuition
- €12,000
Entry requirements and how to apply
- Entry requirements
- Bachelor of Science in Informatics / Computer Science equivalent to the BSc Informatics at TUM Applicants must provide a statement of reasons for their programme choice and a scientific essay. Applicants may be invited for a written test on campus. GRE or Gate Test mandatory for applicants with a degree from Bangladesh, China, India, Iran, and Pakistan Preliminary documentation from uni-assist for applicants who obtained their Bachelor's degree outside Germany For further information, see: https://www.cit.tum.de/en/cit/studies/degree-programs/master-data-engineering-and-analytics/ .
- English language requirement
- C1 required, please provide an official language certificate, e.g.: TOEFL iBT (before 2026): 88 PTE Academic: 65 IELTS Academic: 6.5 Cambridge English Qualifications: C1 Advanced
- German language requirement
- No minimum language level required
- Application period
- The following is valid for applicants from: all countries For the summer semester: until 30 November The following is valid for applicants from: all countries For the winter semester: until 31 May
- Apply through
- Please check this website .
About this programme
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: 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. Technical advances, for example, in multi-core systems and cloud computing, make it possible to examine data sets on a large scale. 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 programme is divided into three areas of study: Data Analysis, Data Engineering and Data Engineering an
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