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RWTHx: A Hands-On Introduction to Process Mining
Compact course to learn the basics of Process mining. After this course, you will understand the concepts and you will be able to analyze event data. Using the provided software, you can immediately start improving any operational process.
This Course Includes
edx
0 (0 reviews )
5 weeks at 3-5 hours per week
english
Online - Self Paced
course
RWTHx
About RWTHx: A Hands-On Introduction to Process Mining
Process mining is an exciting new technology that enables organizations to improve operational processes in a data-driven manner. Process mining can be applied in logistics, finance, production, sales, and healthcare. This hands-on course explains the key concepts and techniques in process mining. You will learn about automated process discovery, conformance checking, performance analysis, and applications of machine learning to event data. The theoretical concepts learned are tool and application-independent. However, to be able to apply these concepts, the course provides several data sets and access to the Celonis process mining software. After taking this compact course, participants understand current trends in process management and automation, know the key process discovery and conformance checking algorithms, and can apply these to real-life data sets using the Celonis software. Moreover, it is clear how comparative and predictive process mining techniques can be used to perform root cause analysis of performance and compliance problems in any domain.
What You Will Learn?
- Understand and apply process discovery techniques .
- Understand and apply conformance checking techniques .
- Process modeling techniques such as DFGs, BPMN, and Petri nets .
- Extracting event data for process mining .
- Compare and analyze operational processes .
- Know the connection between process mining, data science, and machine learning .
- Know the connection between process mining, process science, automation, and process management .
- Use the Celonis process mining software using your own data sets .