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.
In parallel to the master program "Data Engineering and Analytics", the department of Mathematics is creating the master program "Mathematics in Data Science". Both programs share a common core of foundations and focus on the informatics and mathematical aspects of Big Data. The collection, modeling, storage, processing and evaluation of extremely large (e.g. social media), rapidly changing (e.g. sensor data) and complex (e.g. ecological systems) data sets is the aim of both programs. In the Informatics program, emphasis is put on how to provide data, so that is can be efficiently processed with various methods. Data storage and access must be accommodated to the humongous amount of data, the high frequency of change and the high complexity and be able to react variably. Furthermore, it must adjust to changing analytical algorithms and visualization of results. "Data Engineering and Analytics" provides a profound knowledge about the fundamental methods and teaches practical techniques for the processing of very large data sets.
In contrast to this, "Mathematics in Data Science" focuses on computation, simulation and prediction of complex phenomena (e.g. customer behavior, economical trends and medical data) and on the often times complicated interpretation of this data, which requires complex mathematical models.
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 distributes 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.
For further information please refer to the curriculum overview and the module catalogue.
The master program data engineering and analytics is designed for students with a bachelor's degree in Informatics or Mathematics with a minor Informatics (or similar), which aim to specialize in Data Engineering and Analytics. Applicants need to have elementary skills in Informatics, especially foundations of Informatics and programming of algorithms and databases. In the application process, the applicant's transcript of record is matched against a predetermined catalogue. In case there are important core lectures missing, these can be added as mandatory courses to the applicant's curriculum. In case more than 30 ECTS are missing, admission is not possible. Also, proficiency in English is required. Furthermore, students that did not provide proof of proficiency in German before enrollment will receive an obligation to complete at least one module that provides integrative knowledge of the German language. Voluntarily completed extracurricular courses, for example German courses at the TUM language center will be recognized.
For details on prerequisites and please refer to the application process description.