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

 21 Hours

Number of participants



Price per participant

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