This course aims at providing students with tools and experience to analyse real-life data for a real-time, sensitive business intelligence case-study.

PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.

This course is highly technical, and treats the practical aspects of producing real-life Business Intelligence, as well as covering the computational tools to implement this. Tools for the analysis of data are discussed, focusing on tools which emphasise the importance of uncertainty in intelligent decision-making.
We study how (and how not) to build predictive models to frequently extract information from dynamic data, and how to interpret these methods and summaries intuitively and efficiently develop new services for the organisations that provide the data.
These techniques will be implemented with the R open-source software. Cases are selected from the literature and our own research experience.

* Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan.
* https://mc-stan.org/users/documentation/
* Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. CRC Press.
* Other materials will be made available through Student Portal.

Basic Statistics, Regression, Basic R