The course in Predictive Analytics requires participants to have a university degree, but practitioners with equivalent professional skills may also be admitted by the course management. All prospective students must have completed the CAS in Data Analysis as a prerequisite for admission.

The course in predictive analytics takes place once a week, all day, spread over a period of five months.

Continuing education, further training in predictive analytics is aimed at people such as analysts who are responsible for processing company or public data, as well as people who are entrusted with the evaluation of customer data, the monitoring and regulation of operational or production processes or the delivery of statistical models or reports as a data-protected basis for decision-making.

The continuing education course in Predictive Analytics (CAS Statistical Modeling) consists of four modules: "Information Processing with R" with the contents data wrangling and visualization, reporting tools, designing analysis strategy, creating shiny, "Advanced Regression Modelling" with methods of regression calculation of non-normally distributed target variables, model assessment, development of data-protected generalized regression models, "Analysis of Time to Event Data" model suitability, hazard rate, censoring, inference, failure and waiting time models, "Network Analysis" with social network models and properties, community structures and centrality measures, diffusion in social networks, graphical models and causality, visualization of networks and 2-dim embedding.

Graduates of the continuing education, further training in Predictive Analytics receive the "Certificate of Advanced Studies in Statistical Modeling" with 12 ECTS after passing the course.