Data Science for Business & Commerce
level of course unit
Learning outcomes of course unit
Students are familiar with the usage areas of data acquisition, data storage, data analysis and data usage within the context of business-related and digital-commerce applications. They understand the special challenges of this usage area and are familiar with established best practice methods. Furthermore, they are able to independently design and implement data-based applications in this area while taking domain-specific requirements into account.
prerequisites and co-requisites
Students acquire detailed knowledge of the techniques and tools of data science in the area of business and commerce, specifically in:
- Business intelligence and management information systems (e.g. dash-boards)
- Key figure systems and data structures
- Forensic data analysis for fraud detection
- Process mining for procedural optimization/illustration
- Recommender systems (user/item/content-based collaborative filtering)
- Customer profile analysis (e.g. lead scoring, customer lifetime value, etc.)
The purpose of this course is to give students special insight into other areas of data processing and expand their problem-solving horizon
recommended or required reading
- Cady, F. (2017) The Data Science Handbook. 2. Auflage, Wiley, Hoboken (ISBN: 978-1119092940).
- Meier, A.; Stormer, H. (2012) eBusiness & eCommerce: Management der digitalen Wertschöpfungskette. 3. Auflage, Springer, Berlin (ISBN: 978-3-642-29801-1).
- Tamm, G. (2003) Konzepte in eCommerce Anwendungen. 1. Auflage, SPC TEIA Lehrbuch, Kelkheim (ISBN: 978-3935539661).
assessment methods and criteria
Seminar thesis or final examination
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number of ECTS credits allocated
planned learning activities and teaching methods
Lecture with discussion
Performing exercise tasks
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