Duration
21 hours (usually 3 days including breaks)
Requirements
- A basic understanding of logic, sets, and statistics
- Basic computer skills
Audience
- Data science professionals
- Anyone interested in understanding and applying process modeling and data mining
Overview
Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency.
Format of the Course
- The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework.
Course Outline
Introduction
Overview
- Discovering, analyzing and re-thinking your processes
Types of Process Mining
- Discovery, conformance and enhancement
Process Mining Workflow
- From log data analysis to response and action
Other Tools for Process Mining
- PMLAB, Apromoro
- Commercial offerings
Summary and Conclusion