Process Mining: Data Science in Action
School: Eindhoven University of Technology
Registration Link: Process Mining: Data Science in Action
Start Date: April 1, 2015 (6 Weeks Long)
Workload: 4-6 Hours Per Week
Instructor: Wil van der Aalst
Credentials: van der Aalst teaches at the Eindhoven University of Technology, where he chairs the Architecture of Information Systems (AIS) group in the school’s Department of Mathematics and Computer Science. A recognized expert in business process management (BPM), process mining, and data science, he has written or edited 17 books, published over 170 journal papers, and carries an H-index of 112 on Google Scholar. .
Graded: For a $49 fee, students can earn a signed certificate of accomplishment for completing the course.
Description: Process mining “bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.” In this course, students will study analysis techniques and models and apply them using real data sets. In addition, students will examine the three types of process mining: discovery, conformance and enhancement. The courses consists of 8-15 minute videos and readings, with students completing a peer-reviewed assignment that requires them to extract and compare data and write a summarizing report. Students will also be evaluated using weekly quizzes and a final exam.
Review: “I expected to learn the concepts and applications of process mining. All of this was delivered. Course material content AND presentation was excellent – the videos were extremely good – one of the best I have seen so far on courser. The tools were easy to install and easy to use – no programming required. Hands-on Exercises were included – so one could actually create models from logs, analyze the models and became familiar with the functionality of 2 Process Mining tools. Focus was on process discovery, performance analysis, conformance checking What I missed: hands-on excercises for extraction of event-logs (notice: this might be a boring task but is extremely important) – usually the majority of time in data mining projects is spent on getting, understanding, filtering, cleansing data, hands-on exercises for more advanced topics (prediction, recommendation) / operative support settings (combination of RapidMiner / ProM)… it is clear that these things could or should be added in another subsequent course.” For additional reviews, click here.
Additional Note: Students will need specific software to complete the course. They are also expected to have a basic understanding of statistics.