Course Info
| Module [Module Number] | SpM Analytics for Business I [1277MSAFB1] |
| Regular Cycle | Winter Term |
| Teaching Form | Lecture and practice |
| Examination Form | Written test: Portfolio |
| Teaching Language | English |
| ECTS | 6 |
| Instructor | Prof. Dr. Markus Weinmann, Sercan Demir |
| KLIPS 2.0 | Link |
Module Content
- The course on Bayesian Data Analytics provides a broad introduction to the concept of Bayesian statistics and modeling.
- Topics: model building and evaluation, MCMC simulation, generalized linear models, bino- mial/Poisson regression, and multilevel models.
- The course will also discuss recent Bayesian data projects, and students will learn to set up their Bayesian projects using R.
Learning Objectives
Students …
- know and understand the relevant methods and theories for the points mentioned above under „Module content“.
- understand advanced, specialized theories / methods in the area of Bayesian Data Analytics.
- analyse current questions and challenges in the area of Bayesian Data Analytics.
- assess and discuss findings and research results of specialized theories / methods.
- discuss scientific topics in a professional manner and appropriate to the situation with (non-) specialists.
- act responsibly considering ecological, social and ethical criteria.