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Course Outline
Training plan
Introduction
Process Mining Overview
• Examples of analyses
• 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
• Visualization of process models
• Analyzes visualization
• Process model metrics - confusion matrix, fitness and precision
• Compliance testing
• Sojourn time vs waiting time
• bottlenecks
Summary and Conclusions
Requirements
Requirements
• Basic knowledge of programming language Python
• Basic knowledge of issues Data Science
Audience
• Specialists Data Science
• Developers Python interested in expanding their knowledge of methods for automatic process discovery and gaining process insight from data
21 Hours
Testimonials (5)
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
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
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
I mostly enjoyed everything.
