Fundamentals of Process Mining

From Theory to Practice

What you'll learn:
Process Models (BPMN and Petri nets)
Event Logs
Conformance Checking
Log Trace Clustering

Requirements:
Basic knowledge of Python is preferred but not required.
Learners should have an understanding of data structures such as lists and graphs.

Description:
As event data becomes an ubiquitous source of information, data science techniques represent an unprecedented opportunity to analyze and react to the processes that generate this data. Process Mining is an emerging field that bridges the gap between traditional data analysis techniques, like Data Mining, and Business Process Management. One core value of Process Mining is the discovery of formal process models like Petri nets or BPMN models which attempt to make sense of the events recorded in logs. As business decisions rely on these discovered models, it is crucial to ensure the conformance of them with respect to the recorded process executions. This model-to-log comparison is known as Conformance Checking.
This course is journey on the main techniques of Process Mining, from Model Discovery to Conformance Checking through log trace clustering. It contains both theoretical and practical videos with a lot of examples and some exercises. The lab sessions show how to extract information from a log by using both ProM software and pm4py Python library.
The author of this course believes that the price of learning new stuffs should not be a barrier and invite any learner that cannot attend to the course for this reason to contact her. Enjoy Process Mining !

Who this course is for:
Organisations that want to improve their business processes by using the knowledge contained in logs.
Data scientist that want to learn a new perspective of event data analysis.

More Info