Type and Deduction (in German only):
- im Schwerpunkt Business Analytics des Master Betriebswirtschaftslehre
Basic knowledge about the essential data mining methods concerning custering, classification, and regression.
Scope of Deduction
Dr. Kai Brüssau
Mo: 11:00-12:00 Uhr, WiWi 3136/42; 12-14:00 WiWi 1005
In order to participate in this course it is obligatory to register in STiNE during the STiNE registration periods.
Registration for the exams in STiNE well within the registration periods is mandatory (also for students who repeat the exam!).
The course Data Mining focuses on the practical application of data mining methods for business-administrative issues. With the help of various case studies, different data mining methods are applied and the results are analyzed.
The course deals with the question how data for a data mining problem are prepared and processed before a model is created, which represents the correlations in the data. The analysis of results as well as the use of these results for application in business management practice is also of great importance. Thus, the entire business analytics lifecycle is examined.
The course deals explicitly with the following topics:
- process models for solving data mining tasks
- Chatbots and AI
- introduction into the programming language Python and its use for data mining
- data storage and data access
- implementation of classifications, clustering and forecasting by using frameworks
- case studies:
- text mining / natural language processing
- recommender systems
- image recognition
- other data mining tasks
- solution analysis, computation of error metrics and KPIs
- presentation of solutions, e.g. in a web application
- Florin Gorunescu: Data Mining - Concepts, Models and Techniques, Springer, 2011, DOI: 10.1007/978-3-642-19721-5
- Charu C. Aggarwal: Data Mining - The Textbook, Springer, 2015, DOI: 10.1007/978-3-319-14142-8