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
Testimonials (5)
Entire training. Trainer preparation, communication style, prepared exercises. Everything is top-notch.
Michal - AXAXL
Course - Testable Requirements - How to Write Good Acceptance Criteria?
Machine Translated
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
trainer's knowledge and ease to discuss - awesome flow
Piotr Stanik - GP Strategies Poland sp. z o.o.
Course - Fintech: A Practical Introduction for Managers
Trainer develops training based on participant's pace