Process mining is an emerging branch of data science that aims at deriving qualitative and quantitative insights on the execution of organizational processes, based on the analysis of recorded event sequences.
The course features lectures and exercises that focus on the formal foundations, algorithms, and techniques of process mining. Specifically, this course covers aspects such as:
For the above subjects, the course will cover fundamental algorithms as well as advanced, state-of-the-art techniques.
During the exercises that follow each lecture, you will practice through pen-and-paper exercises, as well as implementation and evaluation using open source process mining tools and libraries.
The lectures and exercises are complemented by a practical assignment in which students will work in groups on a project that involves implementation and/
After completing this course, you will be able to:
You are expected to be familiar with the use of Petri nets and BPMN for process modeling, and being able to do basic programming in Python. IS 515 or having experience with process mining are NOT prerequisites.