Course Outline
Training plan
Introduction
Process Mining Overview • Analysis Examples • Process Mining Notation Types • Data (Event Logs) • XES Data Standard
Process Mining w R • bupaR library • Process data structures • Process discovery algorithms (alpha, alpha+, …)
Tutorials • Data transformation and cleaning for Process Mining • Directly-Follows Graphs • Inductive Process Mining • Process model visualization • Analysis 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 the R language • Basic knowledge of issues Data Science
Audience • Specialists Data Science • R programmers interested in learning more about methods for automatic process discovery and data-driven process insights
Testimonials (4)
Professional knowledge from the market provided by an expert
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Machine Translated
The content, as I found it very interesting and think it would help me in my final year at University.