Course Outline

Course Outline

Introduction

Overview of Process Mining
•    Examples of Analyses
•    Notation Types 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
•    Analysis Visualization
•    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

Audience
•    Data Science specialists
•    Python programmers interested in expanding their knowledge about automated process discovery and gaining insights into processes based on data

 21 Hours

Number of participants


Price Per Participant (Exc. Tax)

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