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Course Outline
Training plan
Introduction
Process Mining Overview • Analysis Examples • Process Mining Notation Types • Data (Event Logs) • XES Data Standard
Process Mining w Python • PM4Py library • Data structures for processes • Process discovery algorithms (alpha, alpha+, …)
Exercises • ETL (Extract, Transform, Load) for Process Mining • Directly-Follows Graphs • Inductive Process Mining • Visualization of process models • Visualization of analyzes • 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 Data Science issues
Audience • Data Science specialists • Programmers Python interested in expanding knowledge about methods of automatic process discovery and gaining insight into processes based on data
21 Hours
Testimonials (5)
Zadania do zrealizowania samodzielnie oraz późniejsze wspólne rozwiązywanie
Katarzyna Kopysc-Falenta - CapGemini
Course - Data Analysis with Python, Pandas, and Numpy
Przekazanie wiedzy praktycznej oraz doświadczeń trenera.
Rumel Mateusz - Pojazdy Szynowe PESA Bydgoszcz SA
Course - GUI Programming with Python and PyQt
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
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
