Course Outline

Training plan

Introduction

Process Mining Overview
• Examples of analyses
• 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
• Visualization of process models
• Analyzes visualization
• Process model metrics - confusion matrix, fitness and precision
• Compliance testing
• Sojourn time vs waiting time
• bottlenecks

Summary and Conclusions

Requirements

Requirements


• Basic knowledge of programming language Python
• Basic knowledge of issues Data Science

Audience
• Specialists Data Science
• Developers Python interested in expanding their knowledge of methods for automatic process discovery and gaining process insight from data

 21 Hours

Number of participants


Price Per Participant (Exc. Tax)

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