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)
Overall training. Trainer preparation, communication methods, and prepared exercises. Everything is top-notch.
Michal - AXAXL
Course - Testable Requirements - How to Write Good Acceptance Criteria?
Machine Translated
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Open discussion about various issues and examples of events we may encounter when creating requirements.
Piotr - Nippon Seiki Europe
Course - IREB CPRE Foundation - przygotowanie do egzaminu
Machine Translated
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