Business Analytics: Case Studies (Summer Term)

Business Analytics: Case Studies (Summer Term 2022)

Business analytics (BA) is a systematic approach that applies qualitative, quantitative, and statistical computational tools and methods to analyze data, gain insights, inform, and support decision-making. In this respect, methods from the field of machine learning (ML) have gained particular attention as they give computers the ability to perform tasks without being explicitly programmed to do so. Advances in ML enable the development of intelligent systems with human-like cognitive capacity that penetrate our business and personal life in every conceivable way. This is demonstrated by many diverse examples, such as fraud detection, predictive maintenance, credit scoring, next-best offer analysis, speech and image recognition, or natural language processing.

This course offers students, who already have a fundamental understanding of BA and ML, the opportunity to deepen their knowledge by developing data-driven processing pipelines and applying modern learning algorithms to solve real-world problems from research and practice. Students can either bring their own interesting BA/ML cases or are provided with exciting challenges from a predefined selection. Depending on the availability of open topics, there is also the chance to work on current cases from our collaboration partners.

The course has a strong practical focus and requires a high degree of self-initiative and dedication by the participants. At the beginning of the semester, some conceptual basics are repeated as a refresher. However, the in-depth investigation of relevant methods, procedures and principles required by the circumstances of the individual cases is done independently by the students in self-study. Students are encouraged to work (in groups) on the chosen projects to solve upcoming challenges in cooperation. To monitor the learning progress during the course, open consultation meetings are offered on a continuous basis, in which the applied approaches and procedures can be reflected in a participatory manner. The final results are presented and discussed at the end of the semester.