Data Engineering and Analytics
M.Sc.
Overview
Handling and analyzing very large amounts of data is an urgent problem in many areas of science and industry and requires novel approaches and techniques. The trend towards “Big Data” is caused by a host of developments: Firstly, 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 many more. Secondly, technical advances for example in multi-core systems and cloud computing make it possible to examine data sets at large scale. And thirdly, such amounts of data do not only origin in the “classical” domains like business data, but now are 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 program 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, on the other hand creates a solid starting point for ventures into research.
Which professional opportunities can I take up with this qualification?
The master’s programs “Mathematics in Data Science” and “Data Engineering and Analytics” offer access to many career opportunities including: research, consulting, IT security, systems design, and data science in industry. The respective departments offer Ph.D. positions that are the pathway to a career in research. Typical job profiles in industry include data analysts and data engineers. Data engineers master very large databases and distributed information systems and are responsible for IT security and applied data analytics for structuring data. Data analysts filter and extract information from large data sets based on statistical and mathematical methods and tailor them towards informed strategic decisions
Structure
The program is divided into three areas of study: Data Analysis, Data Engineering and Analytics and Data Engineering. The first area is concerned with fundamentals of understanding and modelling data and the underlying relationships. Data Engineering consists of lectures about the construction of systems that perform efficient and scalable data processing, thus enable the methods of Data Analysis on large data sets.
The curriculum comprises mandatory courses on Data Analysis and Data Engineering. Advanced lectures are offered in these area of studies: Data Engineering contains lectures about distributed systems, distributed databases, query optimization, database systems on modern CPU architectures and high performance computing. Data Engineering and Analytics offers lectures about machine learning, business analytics, computer vision and scientific visualization. Data Analysis is concerned with topics that require solid mathematical foundations: Fundamentals of Convex Optimization, Computational Statistics and more.
Costs
Tuition
HK$97,031.16
(international)
HK$687.30
(home)
Funding
Admissions
Qualifications
Selection takes place through an aptitude assessment procedure. Aptitude assessment is a two-part procedure after the submission of an official application to a program. In this procedure, the TUM school or department determines whether you meet the specific requirements for its master’s degree program.
In the initial stages, the grades you obtained during your bachelor's program, as well as your written documents, will be evaluated using a point system. Depending on the amount of points accumulated, applicants are either immediately admitted, rejected or invited to an admissions interview.
Language requirements
TOEFL
88