On September 30st, 2019, the eleventh run of the free FutureLearn online course ‘Introduction to process mining with ProM’ will start. Join the 14.000 students who enrolled before you and join the course now!

Process mining is a novel collection of techniques that connects the areas of data science and business process management. Using process mining techniques processes can be analysed in great detail. Based on event data (what happened when, by which resource, and for which case), process mining techniques can automatically discover process models, describing the process flow of the majority of cases. Cases can be invoices, purchase orders, clients, or even website sessions. This allows a flexible view on the process from different angles.

Existing process models or guidelines can also be validated against the event data, in order to analyse deviations. Performance and bottleneck information can be projected on process models to easily detect where most time is spent in a process. Also the social network of how resources in a process collaborate and hand over work can be analysed, all based on the event data containing four columns: what, when, for which patient, and by whom.

In this free course you will learn how process mining can provide answers to the most common questions posed regarding processes. The course is setup around the free and open source process mining tool ProM. This allows you to quickly apply what you learn in this course on your own real data! We also provide example datasets, both artificial and from real-life, which are used in tutorials where our free and open source process mining software ProM is applied, by you!

The course ‘Introduction to process mining with ProM’ is an initiative of the European Data Science Academy EU project. Lead educator is Eric Verbeek from Eindhoven University, who specialises in applying process mining in general and the ProM tool in specific. Are you interested? Or do you want to know more? Register for free at FutureLearn for our online course ‘Introduction to process mining with ProM’!

We hope to see you soon!

Kind regards,

Eric Verbeek