Lecture IE 692: Advanced Process Mining (APM)

Why Take This Course

Process mining is an established discipline, applied by most organizations to analyze how their processes actually unfold based on continuously generated event data. This course teaches the core algorithms behind the field, from automatic process discovery to predictive process mining, complemented by practical exercises in Python in which you analyze real-world event data.

Format	Lecture + Exercise
Credits	6 ECTS
Term	Every spring summer semester (FSS)
Assessment	100% final exam
Prerequisites	None formally required
Language	English
Target Groups	Business Informatics (M.Sc.), Mannheim Master in Data Science (M.Sc.)
Lecturer	Prof. Dr. Daniel Schuster

Topics Covered

Process Discovery
Conformance Checking
Handling Event Data
Predictive Process Mining
Partially-ordered Event Data
Object-centric Process Mining (OCPM)

Prerequisites

There are no formal prerequisites for this course.
Students should, however, be familiar with Petri nets and BPMN models, as these notations are used throughout. A recap of both will be given in the exercise session during the first week, so prior exposure is helpful but not essential.
Basic knowledge of Python programming is required for the practical exercises.