Process Mining: Data science in Action is a Coursera course from Eindhoven University of Technology, taught by Wil van der Aalst. It introduces ways to use recorded event data to discover how a process works, check whether observed behavior matches a model, and analyze performance. Coursera currently presents it as intermediate and self-paced; its six-module estimate is two weeks at 10 hours per week, not a guaranteed completion time. The “April 2015” wording comes from a March 24, 2015 roundup of business MOOCs that included the course, rather than evidence of the course’s original launch date.
What the course covers
Process mining connects event data generated by operational systems with models of the processes those systems record. The course focuses on what can be learned from event logs and how to select methods that answer a practical question. Coursera lists learning goals including process-mining techniques, discovery, conformance checking, bottleneck analysis, identifying what event data can answer, and operational support such as prediction and recommendation.
Its six modules cover event logs, Petri nets, process-discovery algorithms and their limitations, alternative discovery methods, conformance checking, and how to obtain suitable event data. The course outline also names ProM and Disco; their current availability and commercial terms are not established here.
How the main techniques fit together
Process discovery
Discovery uses an event log to derive a process model: a representation of the activity and paths reflected in the recorded data. Its usefulness depends on whether the log captures the relevant events and whether the selected method suits the process and question being studied. A discovered model represents the data available to the analysis; it should not automatically be treated as a complete account of how work ought to happen.
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Conformance checking
Conformance checking compares recorded behavior with an existing process model. This helps identify where actual traces align with or deviate from the model. The comparison is only as meaningful as the event data and reference model: missing, unsuitable, or poorly defined inputs can limit what the analysis establishes.
Performance analysis and operational support
Process mining can go beyond the sequence of activities. The course describes extending analysis with information such as bottlenecks and performance, and introduces operational support, including prediction and recommendation. Which method is appropriate depends on the process, the event data available, and the decision the analyst needs to inform.
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Format, level, and time estimate
Coursera currently describes the course as intermediate and self-paced, offered by Eindhoven University of Technology and taught by Wil van der Aalst. The platform lists six modules and estimates two weeks at 10 hours per week. That is Coursera’s estimate, not a promise that every learner will finish in that time. Enrollment conditions and the course page’s presentation may change.
Coursera’s page, accessed in 2026, displayed 97,587 enrolled learners and 1,274 reviews. Those are changeable platform figures, not evidence of course outcomes or effectiveness.
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Who may find it useful
The subject is relevant to readers who work with operational event data and want to understand how models can be derived from it or checked against recorded behavior. Before enrolling, consider whether you can access appropriate event data for practical work and whether your goal is discovery, conformance, performance analysis, or operational support. Coursera labels the course intermediate, but the available description does not specify a detailed prerequisite list.
Course versus the related textbook
Wil van der Aalst’s Process Mining: Data Science in Action, second edition, is a related textbook, not a stated purchase requirement for the Coursera course. Springer lists the hardcover ISBN as 978-3-662-49850-7 and publication date as 26 April 2016. Eindhoven University of Technology’s research portal describes its coverage as extending from discovery through predictive analytics, including conformance checking and practical tools.
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What “April 2015” means
The April 2015 reference is supported by a March 24, 2015 business-MOOC roundup that included this course among offerings for April. That source establishes the roundup context; it does not show that the course first launched in April 2015. The course is currently presented on Coursera under the title Process Mining: Data science in Action.
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