Decision Support Systems (Data Science) - (B-F)-M
Course code
QUANT511
Course type
MSc Course
Weekly Hours
2,5
ECTS
5
Term
HS 2023
Language
Englisch
Lecturers
Juniorprof. Dr. Irina Heimbach
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.
Course content
- Introduction to DSS
- DSS and related concepts
- Visual analytics with Tableau
- Text mining, sentiment analysis
- Introduction to data science and predictive analytics
- Logistic regression
- Decision trees
- Introduction to artificial neural networks
- Practice with RapidMiner
- Expert systems and recommenders
- Chatbots, virtual personal assistants, and robo-advisors
- Implementing DSS
- Legal, privacy, and ethics issues
- Impact on organizations, jobs, and works
- Technology trends and future of DSS
Class dates
| Date | Time |
|---|---|
| Monday, 30.10.2023 | 15:30 - 18:45 |
| Friday, 10.11.2023 | 15:30 - 18:45 |
| Tuesday, 14.11.2023 | 11:30 - 15:15 |
| Monday, 20.11.2023 | 15:30 - 18:45 |
| Friday, 24.11.2023 | 13:30 - 17:00 |
| Tuesday, 05.12.2023 | 08:00 - 11:15 |
| Friday, 08.12.2023 | 11:30 - 17:00 |
Learning outcomes
Improved data literacy and data-analytic skills
Improved working skills with Tableau and RapidMiner
Understanding of main concepts of technology-mediated decision support
Literature
Sharda et al. 2021 Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, 11th edition, Pearson.
Learningmethods
Lecture
Integrated exercises and in-class discussions
Practice with software (Tableau and RapidMiner) and data sets
Quizzes
Project work
Exam
Project work (50%)
Final exam (50%)
Total workload
150