Process Mining w Python Training Course
Description
Extending Process Mining analytics using Python tools allows for greater flexibility when a deeper insight into data from processes is required. Process Mining itself (Polish: Eksploracja procesów) is a technique that applies algorithms to event logs to analyze business processes. Process Mining connects data with processes and provides insights into trends and patterns affecting process performance.
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
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Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
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