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Course Outline

Training Agenda

Introduction

Overview of Process Mining
•   Examples of analyses
•   Types of notations used in Process Mining
•   Data (Event Logs)
•   XES Data Standard

Process Mining in Python
•   The PM4Py library
•   Data structures for process modeling
•   Process discovery algorithms (Alpha algorithm, Alpha+, …)

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

Summary and Conclusions
 

Requirements

Prerequisites


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

Audience
•   Data Science specialists
•   Python developers interested in expanding their expertise in automated process discovery techniques and gaining data-driven process insights

 21 Hours

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