Our Teaching Concept and Portfolio in the Master's Programme
According to our research focus, the teaching portfolio of the master's programme consists of various courses in the interface of digital business models and systems, data analytics, web and service analytics, Customer Relationship Management (CRM) as well as a project module comprising topics of business informatics.
We attempt to structure all events as practical as possible by including several practically oriented presentations and case studies within our lectures and tutorials. In the following, a short description of our master courses.
Current teaching portfolio in the Master
Winter semester
Customer Relationship Management (L/T)
This lecture provides students with competences in planning & processing of comprehensive tasks in CRM. It focuses intensively on the operation of IT and data science in the areas analytical CRM, web-based systems and social media CRM.
Summer semester
Data Analytics Challenge
This course teaches the procedure for the systematic design, implementation, evaluation and adjustment of solutions to tasks in the field of business analytics and data mining. For this purpose, students work in moderated teams to develop data analytic process models and methods, for example for a task available on the Internet or a task provided by practice organizations, including the data provided for this purpose.
Data Science Concepts (Lecture Course) (together with the Chair for Statistics and Quantitative Methods)
Students develop the skills to independently acquire new knowledge and new working methods in the field of theory and application of practice-relevant methods of machine learning, to work towards results in a goal-oriented manner and to discuss them in a well-founded manner. In particular, the course covers the following topics: Overview of data science methods and procedures of statistical learning, data spaces and data geometric basics of statistical learning, bias-variance trade-off and error decomposition, supervised learning and feature evaluation, models for dimensional reduction.
Data Science Methods (L/T) (together with the Chair for Statistics and Quantitative Methods)
This course teaches skills in procedures and methods of statistical/machine learning from data. The language R is used in the exercise. In addition to data geometric basics of data science methods and basic methodology, models for dimensionality reduction and feature reduction as well as model interpretation and feature evaluation are considered and tested in the course.
Digital Business Models and Technologies (L/T)
This lecture teaches technical and algorithmic basics of - and business models in - the Internet and Internet of Things. It emphasizes, how the perception of customers, data, value proposition, platform and innovation changes and how to design business models accordingly. Furthermore, business-modeling workshops with partner companies, moderated group works as well as computer tutorials take place.
Project Modules Business Informatics
In this module students work on current practical and research topics in business informatics. Students will independently develop and work on topics that usually include a design and "hands-on" implementation and evaluation part. The result is a seminar paper, a short presentation and the software developed in the project.
The detailed list of topics will be available here from March 27 (only available in German).
Service Analytics (L/T)
The lecture deals with the methodology for the personalization of online contents, services and campaigns in the web and social media. The lecture focuses on modern recommender systems, textual analysis and active learning. Tutorials and exercises are conducted with a modern statistical programming language such as Python or R.