This learner has completed ID verification. Find out more.
Certificate of Achievement
has completed the following course:
Introduction to Process Mining with ProM
Eindhoven University of Technology
This online course covered process mining analysis using ProM. During the course several topics were covered, from event log basics, to process discovery, process model evaluation, alignments, performance analysis and social network analysis. The course was completed with a practical assignment.
4 weeks, 3 hours per week
Joos Buijs
Assistant Professor
Eindhoven University of Technology
Transcript
Learning outcomes
- Identify event data suitable for analysis
- Apply various process mining techniques in the ProM lite tool to event logs
- Interpret the results of various process mining techniques in the ProM lite tool
- Describe the process flow, based on the event data
- Improve processes based on process mining analysis of the event data
Syllabus
Process mining provides a critical, process-centric perspective on data, which is not available with classical data mining or machine learning techniques. For example, with process mining you can:
- analyse how people use public transportation;
- verify whether the loan application process in a bank is executed correctly;
- gain insights in customer journeys on a website and combine this with interactions on different channels such as phone and e-mail;
- analyse learning behaviour of students in a MOOC to improve course contents;
- predict when hardware parts are likely to fail.
Week 1 introduced process mining and showed that event data is everywhere. Furthermore, it explained how you can translate event data that you might have for use in our tool. We also discussed how to start investigating the event log and how to filter the event log based on these insights for further analysis.
Week 2 discussed process models and how to evaluate how good a process model is with respect to the data. We also demonstrated several techniques to automatically learn a process model from the data, and discussed the pros and cons of each approach.
Week 3 covered additional process mining techniques such as conformance checking, performance analysis and social network analysis. This week also showed process mining application examples.
Week 4 provided learners with time to work on the peer assignment where they were asked to write a real process mining report on real-life event data.
After this course learners can:
- work with the free and open source process mining tool ProM;
- process raw event data into an event log for further analysis;
- execute core process mining analysis techniques, and correctly interpret the results;
- gain concrete and actionable process insights from (their own) event data.
Issued on 24th March 2017
The person named on this certificate has completed the activities in the transcript above. For more information about Certificates of Achievement and the effort required to become eligible, visit futurelearn.com/proof-of-learning/certificate-of-achievement.
This certificate represents proof of learning. It is not a formal qualification, degree, or part of a degree.