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Course Outline

Training Plan

Introduction

Process Mining Overview
• Analysis examples
• Types of notations used in Process Mining
• Data (Event Logs)
• XES data standard

Process Mining in Python
• PM4Py library
• Data structures for processes
• Process discovery algorithms (alpha algorithm, alpha+, ...)

Exercises
• ETL (Extract, Transform, Load) for Process Mining
• Directly-Follows Graphs
• Inductive Process Mining
• Process model visualization
• Visualization of analysis
• Process model metrics - confusion matrix, fitness and precision
• Conformance checking
• Sojourn time vs waiting time
• Bottlenecks

Summary and conclusions
 

Requirements

Requirements


• Basic knowledge of the Python programming language
• Basic understanding of Data Science concepts

Target Audience
• Data Science specialists
• Python programmers interested in broadening their knowledge of automated process discovery methods and gaining insight into processes based on data

 21 Hours

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