Data Science for Business & Commerce
Niveau
Master's degree program
Learning outcomes of the courses/module
The participants:
• can identify fundamental areas of application for data collection, data storage, data analysis, and data use in the context of business applications
• can understand the particular challenges of this field of application and can describe established best-practice methods in this area
• can design and implement data-based applications in this area themselves, taking domain-specific requirements into account
• can identify fundamental areas of application for data collection, data storage, data analysis, and data use in the context of business applications
• can understand the particular challenges of this field of application and can describe established best-practice methods in this area
• can design and implement data-based applications in this area themselves, taking domain-specific requirements into account
Prerequisites for the course
No prerequisites
Course content
The following topics are covered in the course:
- CRM at the strategic level
- CRM in process management
- CRM at the operational level (CRM software systems)
- Operational CRM
- Analytical CRM
- Communicative CRM
- CRM at the strategic level
- CRM in process management
- CRM at the operational level (CRM software systems)
- Operational CRM
- Analytical CRM
- Communicative CRM
Recommended specialist literature
- VanderPlas, J. (2023): Python Data Science Handbook: Essential Tools for Working with Data (Ed. 2), O'Reilly, (ISBN: 978-1098121228)
- Cady, F. (2017): The Data Science Handbook (Ed. 2), Wiley, Hoboken (ISBN: 978-1119092940)
- Saporta, G.; Maraney, S. (2022): Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, Using SQL and Python (Ed. 1), O'Reilly, (ISBN: 978-1492093329)
- Meier, A.; Stormer, H. (2012): eBusiness & eCommerce: Management der digitalen
- Cady, F. (2017): The Data Science Handbook (Ed. 2), Wiley, Hoboken (ISBN: 978-1119092940)
- Saporta, G.; Maraney, S. (2022): Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, Using SQL and Python (Ed. 1), O'Reilly, (ISBN: 978-1492093329)
- Meier, A.; Stormer, H. (2012): eBusiness & eCommerce: Management der digitalen
Assessment methods and criteria
Seminar paper
Language
English
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Interactive workshop
- Case studies
- Lecture with discussion
- Interactive workshop
- Case studies
Semester/trimester in which the course/module is offered
3