Our main aim in this section is to inform you about our practical courses at Technical University of Munich. For any course-related or technical-related questions, you can always reach out to us here.
The Practical Courses We Offer to Students at TU Munich
Designing IT-based Learning
The participants develop an IT-based learning unit for selected MINT-topics out of the curriculum of the senior years of secondary school and conduct this learning unit at a school class.
Developing Innovative Services at the Example of SAP Technologies
This course aims at students who are interested in insights into SAP Cloud Platform and SAP HANA. The course goal is to teach general concepts of in-memory databases at the example of SAP HANA and hands-on experience in relevant SAP technologies in this context. Also, topics like IoT, big data, machine learning and blockchain are introduced.
Enterprise Software Engineering at the Example of SAP
The practical lab course “Enterprise Services” is aimed for students who want to become more familiar with both ABAP programming and applied web technologies (Web Dynpro, BSP, SAPUI5). The course goal is to teach general concepts of the ABAP programming language. In addition, it allows to explore the different web technologies and to gain individual experience in programming skills. The participants learn to handle the development environment Eclipse. Simultaneously, they are enabled to create simple applications with user dialogs as well as database accesses and to understand and get acquainted with the inner workings of business application programming.
Machine Learning for Information Systems Students
Artificial intelligence and machine learning are the most growing topics of our time. The steadily increasing data growth combined with the ever shorter becoming time for software product releases in particular requires ever more effective and efficient data analyzes. Learning algorithms are already influencing our working world and our leisure time. In this practical course we want to deal with the basics of machine learning. In addition, we want to practice an entire machine learning pipeline and carry out our own short machine learning project based on practically relevant problems.