Process Mining w Python Training Course
Description
Extending Process Mining analytics with tools in Python allows for greater flexibility when a deeper insight into data derived from processes is required. Process Mining is a technique that applies algorithms to event logs to analyze business processes. It integrates data with processes and provides insight into trends and patterns that affect process performance.
Course Outline
Training Plan
Introduction
Process Mining Overview
• Analysis examples
• 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
• Process model visualization
• Visualization of analysis
• 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
Target Audience
• Data Science specialists
• Python programmers interested in broadening their knowledge of automated process discovery methods and gaining insight into processes based on data
Open Training Courses require 5+ participants.
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Testimonials (2)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
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