Chevron Left
Back to Process Mining: Data science in Action

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

4.7
stars
1,218 ratings

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

RK

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.

PP

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.

Filter by:

1 - 25 of 310 Reviews for Process Mining: Data science in Action

By Eric R

•

Apr 2, 2021

I've completed the course with Honours, and If you wish to learn Process Mining, this is the course for you. I do however, have a number of problems with the learning material, which is why I've given 3 stars.

First, learning ProM, one of the Process Mining tools (the other being Disco). ProM is meant to be a more flexible and powerful tool, and is considerably more difficult to understand than Disco. However, Disco actually got its own video series explaining the details of how it works, with a step-by-step walkthrough. The ProM walkthrough was hidden inside the Honours Tool Quiz. There is a single video before that which gives an overview of ProM, but that's all it is, an overview.

In my personal opinion, the real reason ProM is difficult, is because it has a confusingly designed interface and little in-app explanation of its workings. This is, however, besides the point, and I'm not letting this impact the review of the course itself. I would just like to contest the concept of something necessarily being more difficult to learn just because it can do more.

Onwards to my second main gripe with this course - I was not impressed by the quality of the learning material. In one particular instance, when they introduce the Alpha algorithm, they begin by dumping 8 lines of dense set theory. Mind you, I'm already familiar with set theory, but I feel sorry for anyone who does not. I would have appreciated a more intuitive explanation.

Mind you - as I said before, if you are interested in Process Mining, this is a course you want to try. I just have high standards for teaching materials.

By Andrei I

•

Feb 27, 2019

I took the course to extend my knowledge of data mining and to apply it to a more business setting. I think the course does a great job to balance dry theoretical concepts (such as Petri Nets and other modelling notations) and business aspects (such as the holistic view of data and processes and the interpretation of results).

When preparing for applying for a process mining research position I reviewed every lecture and got to understand even more some aspects that didn't resonate with me on first viewing. It also helped me to dive into some process mining papers in between (such as the papers recommended at the end of some lectures). The more you encouter some concepts explained and used in different ways, the better you understand them.

If you are like me and want to add another layer on top of the data mining/data science knowledge and have some business ambitions, I would definitely recommend the course to you!

By Anurag G

•

Dec 31, 2019

This is a phenomenal course and highly recommended for anyone interested in learning the next big thing in processes. I am a Lean Six Sigma Master Black belt and manage a Process Excellence team at a large corporation. I found the course content to be hugely meaningful in enhancing my learning of how data science tools used in Process Mining can meaningfully help solve real world problems. Professor Wil van der Aalst is truly a Guru in the field and his team must be complemented for conducting such a useful course on Coursera. The only note of caution is the course is challenging and it takes quite a bit of effort to learn the concepts and successfully complete the quiz. However the effort is totally worth it, rewarding and without this level of effort it will be very hard to understand and apply the concepts of process mining in real world. Hats off again to the course instructors!

By Mahsa R

•

May 11, 2018

It is too conceptual. I watched all these long videos and I still don't know how to do a real process-mining project for a client.

By Alessandro T

•

May 13, 2018

Very interesting course, explained in a understandable way and rich of high level topics. Essential for anyone who likes statistics and process analysis. Many congratulations for it!

By Enrique C

•

Jul 31, 2017

Great course. Professor Wil van der Aalst delivers great lectures, very clear and deep in general with good examples. I really enjoyed the course from the beginning to the end.

By Alex G

•

Jan 29, 2019

Great overview of the Process Mining field. Easy to follow and very intuitive course material. Great usage of exercises and examples. Helpful practical introduction to Process Mining tools.

By Michael S T

•

Jan 28, 2018

This course was wonderful. I have attempted it several times, but did not find enough time to finish it until lately. Dr. van der Aalst is magnificent in his presentation.

By Joseph D B

•

Jul 19, 2016

Very beautifully done: information very well and clearly organized, illustrated, presented, and referenced. Friendly approach to a genuinely useful topic.

By Vishal

•

May 19, 2022

Excellent Course, great introduction to process mining. Look forward to more courses in the future!! The field has great many applications.

By Alexander F P L

•

Dec 9, 2018

Really good course, I could apply the knowledge I acquired direclty for my job.

By Felipe M P L

•

Jul 23, 2017

Too much time going over details of the models and not enough on practical use

By Max F

•

Nov 1, 2018

I wish there was more hands on experience using the software

By Waleed A

•

Nov 12, 2017

This is really a great course. a new field which could help any one to find a better position at work and it will help in performing the most common process mining activities. I would recommend this course for any one who is interested to know more about process optimization and discovery. furthermore the course will slightly helps to conduct a process mining project. Many thanks to Wil van der Aalst and to everyone who supported to bring this course.

By Ivan A

•

Feb 23, 2018

Excellent course! I really liked how the complex nature of Process Mining is explained with examples.

Both theoretical and practical sides of Process Mining are explained.

References to more specialized and advanced materials were given so that one can further research for particular needs.

Great work Wil!

I would really enjoy to see a course like "Comparative Process Mining" or "Advanced Practical Process Mining Applied" from you.

Thank you very much!

By Lian K

•

Sep 21, 2022

This is one of the best courses I had the pleasure of attending. I have watched every episode multiple times and each time I have learned something new. I want to thank professor Aalst and Eindhoven University for the production of such a wonderful course.

By Dave v P

•

Mar 31, 2019

I loved this course! I learned so many different parts of Process Mining and will definitely use this in my work. Sidenote: The enthusiasm kept me going. Hope to see you soon and otherwise, see you next time!

By Edwin V

•

May 28, 2018

Best online course Ive ever taken. Great details and lots of specific examples. Perfect theory and practice balance. Really satisfied! Congratulations for you for this example of how to set an online course!

By Ranjit K

•

Jul 2, 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.

By Petri E

•

May 19, 2022

Excellent course. Great lectures providing a thorough overview of the topic area. The quizzes do a good job in ensuring that the message has been received.

By Jiaxin C

•

Oct 3, 2017

Outstanding course structure, even for someone like me that have absolutely no background in process mining, to learn so much in this course:)

By Zoltán N

•

Jul 20, 2017

My first MOOC where I felt that really it meets the standards of a real-life university course. Broad, deep, challenging, highly recommended.

By Maksim C

•

May 15, 2018

Great course about general principles of process mining! It gives many insights. Consideration of PM tools is very useful. Thanks!

By Yosef A

•

Jan 21, 2018

Easy-to-understand with useful examples, and also process mining is a technique that is applicable to many cases.

By shiyangqi

•

Jan 26, 2017

the course looks into process analysis, which is also a very important section of data science!