Introduction to Process Mining:
Overview of Process Mining and its applications
Basic concepts and terminology
Introduction to popular Process Mining tools
Understanding event logs and process models
Data Preparation for Process Mining:
Data sources for Process Mining (e.g., event logs)
Data quality assessment and preprocessing
Handling different types of event logs (e.g., structured, semi-structured)
Data transformation and enrichment techniques
Process Discovery:
Techniques for automated process discovery
Process model representation (e.g., Petri nets, BPMN)
Applying Process Mining algorithms for discovery
Evaluating discovered process models
Conformance Checking:
Checking process model compliance with event data
Deviation analysis and identifying process inefficiencies
Metrics for conformance checking
Interpreting conformance checking results
Process Enhancement and Improvement:
Process analysis techniques for performance improvement
Root cause analysis using Process Mining
Identifying bottlenecks and inefficiencies
Process optimization strategies based on Process Mining insights
Advanced Process Mining Techniques:
Complex event processing (CEP) for real-time analysis
Predictive Process Mining
Handling large-scale event data
Advanced algorithms and methodologies in Process Mining
Process Mining in Specific Industries:
Industry-specific case studies and examples
Tailoring Process Mining techniques for different domains (e.g., healthcare, finance, manufacturing)
Addressing industry-specific challenges and requirements
Process Mining Tools and Platforms:
In-depth exploration of popular Process Mining tools (e.g., Celonis, Disco, ProM)
Hands-on training with tool functionalities
Choosing the right tool for specific use cases
Integrating Process Mining tools with existing IT infrastructure
