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Learner Reviews & Feedback for Process Mining: Data science in Action by Eindhoven University of Technology

994 ratings
260 reviews

About the Course

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. 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. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining. 1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log. 2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. 3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases. Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field. After taking this course you should: - have a good understanding of Business Process Intelligence techniques (in particular process mining), - understand the role of Big Data in today’s society, - be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification, - be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools), - be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools), - be able to extend a process model with information extracted from the event log (e.g., show bottlenecks), - have a good understanding of the data needed to start a process mining project, - be able to characterize the questions that can be answered based on such event data, - explain how process mining can also be used for operational support (prediction and recommendation), and - be able to conduct process mining projects in a structured manner....

Top reviews

Jul 1, 2019

The course is designed and presented by professor aptly for beginners. I think before reading the Process Mining book it is good to take this course and then read the book later. The quizzes are good.

Dec 9, 2019

Good content, very thorough, and I learned a LOT! Took more time than suggested, as I learn by taking notes and reproducing diagrams. But the course structure allowed for frequent pauses to do this.

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226 - 250 of 258 Reviews for Process Mining: Data science in Action

By sharath

Feb 5, 2019

Gives a solid foundation for the process mining concepts!! Explained in depth by a wonderful professor.

By Amarildo J F d L

Jun 15, 2020

O fato de não ter tradução para português impactou bastante na compreensão de algumas atividades

By Rine l C

Apr 10, 2021

Very interesting course. Theory in the book goes quite deep, but it shows a lot of potential.

By Alberto C B

Oct 15, 2017

Best course out there in Process Mining! The professor explains the topic in a very good way.

By Felix G

Jul 25, 2021

s​ome tasks/assignments were a bit cubersome to navigate but overall a great course

By Jesús R S

Jun 15, 2018

Good approach to an interesting topic and extensive practise exercises with tools.

By Tania K

Sep 18, 2017

Great course. A lot of academic knowledge but also covers practical experience

By A M

Dec 11, 2020

Good course. Gives a nice overview over the topic Process Mining

By Chow K M

Jun 23, 2021

M​ore examples to explain calculations would be helpful.

By Schuffenecker

Dec 3, 2019

a bit academic in the beginning, but really interesting.

By Dardo G

Jun 5, 2017

Very interesting, practical and full o information.

By Viktoriia

Mar 5, 2019

I think practical tasks in ProM should be included

By Robin G

Jun 23, 2017

Very clear presentation and a lot of examples


Jan 22, 2020


By Eric S

Jun 26, 2021

Useful information for my work

By Lerata M

Jun 1, 2021

Challenging and fun

By Rob v d L

Oct 15, 2017

Excellent course!

By Gabriel E

Mar 4, 2017

Great course!


Apr 15, 2019

Good Course

By Sofie H

Sep 7, 2018

Sometimes too technical. It would have enjoyed it more if there would have been the possibility to choose which aspects of Process Mining I was interested in. In one of the last lessons, I came to the understanding that I want to apply process mining to spaghetti-structured event data, therefore I had to learn a lot about prediction, recommendation and so on which than turned out to be completely useless for me. I have the same feeling for the petri net, workflow syste, BPMI and so on that are presented; this is only useful for some users while this takes a large part of the study time. It may thus be recommended to organize a more 'practical' PM course for users interested in using Disco and a more technical course for users interested in more advanced analysing techniques.

By Sylvie B

Jan 12, 2020

Significant learnings on content which appears rationale but proved not to be in modules 2 and 4. The concepts of deadlock, soundness, live is not well explained. The time required is significantly more than advertized by Coursera. The quiz are not timed, even though there is a time indication. Some questions on the quiz have multiple correct answers, which are sometimes very subjective and tricky to get right. Lots of mental gymnastics and computation. Sadly, the concept of peer-graded assignment to strive for honors roll does not work well as the number of learners at anytime is very few, if any. The honors assignment on module 4 is very lengthy and regrettably cannot be completed satisfactorily because of software issues.

By alex b

Oct 20, 2018

Some topics are a bit glazed over and others with concepts that are acknowledged to have major shortcomings (e.g. the alpha algorithm) have a heavy focus in the course and exam despite these shortcomings. Frequent notational switches ("we can automatically change this to ___ ") can make some lectures harder to follow as well, if you're not perfectly versed in some of the leveraged notations in this course. OK overall.

By Martin S

Dec 4, 2018

Good introduction to theory of process mining, but most of the techniques are problematic and therefore not practical, and the test questions are tedious as they focus on testing whether you can remember the theory rather than how to apply the theory to real-world problems.

By Niko M

Feb 4, 2019

Very good course. More real life cases and process mining examples would be beneficial.

By Babak N R

Aug 14, 2021

It should be more practical and the screenshots quality needs to be improved