Applied Computer Science (M.Sc.)

Applied Computer Science (M.Sc.)

A new class of IT applications emerged gaining immense attention and substantially changed the social perception of Computer Science significantly. At the same time, new challenges arose from this class of smart systems altering methods for the design, development, and operation of IT systems in a fundamental way.

The new challenges are deeply anchored with the main characteristics of these smart systems and applications:

Mobility: Mobile systems are moving while providing or consuming services. Generally, mobile users take mobile devices with them in order to utilize wireless communication services. Mobile Applications exhibit specific behaviors and are constrained in ways that were unknown in traditional IT systems. They have to be developed for the economic use of scarce resources and at the same time to adapt gracefully to context changes.

Distribution: Modern IT systems tend to be highly distributed. The adoption of mobile devices and wireless communication technology makes it possible to access services located globally. In consequence, the resulting complexity has to be met with adequate concepts, methods, and technologies.

Contextual dependence: Due to mobility and rapidly changing environments applications have to adapt themselves to situational changes quickly. Smart applications are expected to deliver meaningfully and adapted variants of their services in a different types of environments (context awareness).

Central Topics of Applied Computer Science

• Distributed and Mobile Systems. Central Topics include: Concepts and methods for the development of distributed and mobile systems, Software engineering, programming of mobile applications and communication technologies, Advanced concepts for the engineering of distributed systems as well as modern integration technologies and middleware platforms.
• Knowledge Engineering and Data Science. For knowledge engineering and data science, the autonomous interactions to several kinds of environments providing data is of basic importance. The courses provide build up a deep knowledge, of basic to advanced topics as well as practical abilities in data acquisition, digital signal processing with emphasis on time series processing and prediction, feature extraction methodologies, and machine learning technologies.
• Software Engineering. Central Topics include: Interdisciplinary and holistic techniques for the development of smart systems. This affects the design, development, testing, and operation of these applications.
• Communication Technologies and Security: Distributed and mobile systems are highly communication intensive. Solid knowledge of underlying communication technologies is a prerequisite for the development of these systems and their security.