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

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.

This course has been cancelled for the master Digital Business and Economics.
See the Master Education and Examination Regulations for more information.


This course is in transition for the master Business Research.
See the Master Education and Examination Regulations for more information.

The following rule applies to master Business Research students who started the programme prior to academic year 2024-2025.
TRANSITIONAL REGULATION (EBC4107):
The master Business Research has been discontinued.
Courses of the Business Research master’s programme will continue to be offered until and including academic year 2025-2026 with exam opportunities running until and including 2026-2027.


PREREQUISITES:
* Basic Statistics
* Regression
* Basic R