The course covers the basic concepts of computer programming.

This course provides the foundations of programming and working with data. Computer Science II particularly stresses code efficiency and provides the basis for understanding, design, and analysis of algorithms and data structures. In terms of working with data, foundations required for understanding experimental data and notation and basic concepts for machine learning are covered.

Computer Science II lays the foundation for understanding, designing, and analyzing algorithms and data structures.It also provides an overview of various programming concepts, such as functional programming and static and dynamically typed programming languages.

This course provides the foundations of programming and working with data. Computer Science II particularly stresses code efficiency and provides the basis for understanding, design, and analysis of algorithms and data structures.

In preparation for scientific work, especially the physics lab courses as well as semester and master's theses, students receive an introduction to many relevant aspects of data acquisition (measurement technology), software-aided data processing (error calculus, statistics, comparison with models up to machine learning) and data representation (graphs, interpretation).

This course provides a fundamental training in the areas of data science and machine learning. It is intended for managers and leaders who want to understand the typical workflow, fundamental techniques and key challenges of data science and machine learning to drive successful implementations.

This course (e-learning module and face-to-face sessions) equips doctoral students with knowledge and tools to recognize, discuss and address ethical issues of their research.

This course introduces both theoretical and practical aspects of software engineering, all of which are applied in a substantial team project.

This course provides a comprehensive overview of the software development process, introducing participants to essential techniques for facilitating the delivery of high-quality software products. The knowledge and practical experience gained will help managers to improve communication with software development teams, ultimately leading to higher success rates.