Process Mining w Python Training Course
Description
Extending Process Mining analytics with Python tools offers significant flexibility when deeper insights into process-derived data are required. Process Mining itself is a technique that applies algorithms to event logs to analyze business processes. It integrates data with processes, providing visibility into trends and patterns that influence process performance.
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
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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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