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Process Mining: Data science in Action に戻る

アイントホーフェン工科大学(Eindhoven University of Technology) による Process Mining: Data science in Action の受講者のレビューおよびフィードバック



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....



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.


Jan 29, 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.


Process Mining: Data science in Action: 1 - 25 / 137 レビュー

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 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 Radu-Andrei C

Jul 13, 2019

I am surprised to have learned so many new topics and methods for data science in one course. It's like opening a pack of trading cards (e.g. Pokemon TCG or Yu-Gi-Oh cards) and finding that you don't have any duplicates. I think the knowledge in this course is a great addition to the skill set of any data scientist (regardless if you currently work or not with processes). Finally kudos to Prof. van der Aalst and his team. Very well planned lectures, quality content and no boring quizzes. I think it would be good for the future to add more quiz variations, as taking a quiz twice to improve one's answer one will work with exactly the same questions and numbers. I would also like to see a future Process Mining course, with more in-depth lectures on the topics of conformance checking and enhancement. A more practical side would also be welcome, for example coding some algorithms.


Jul 03, 2019

Excellent cours, pour peu que l'on ait une fois suivi un cours de data mining, on voit très vite une chance de se spécialiser. De même que pour un business process analyste, il en ressort une nette opportunité d'étendre son champ d'expertise.

by ranjit k

Jul 02, 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 Paulien L

Jun 10, 2019

I'm a novice to data science and took this course after an (offline) post graduate education Big Data Analyst. I learned about Disco during that training. With this Coursera-course I wanted to know more in detail about procesmining.

Though it was quite jejune and theoretical sometimes I found it interesting and doable enough. With the exams, practising and assignment alltogether I feel it did come to live as well. So I made it to the end and feel happy and proud to complete this course. Many thanks to the team om TU/e!

by Mustafa G

Jun 01, 2019

Very good course overall. I wish there was more technical lessons in the last two weeks

by Mohibullah K

May 15, 2019

Very practical oriented course on Process Mining.

by Kerim A

Apr 18, 2019

very informative, amazing content, and definitely worth it. Thanks for offering such an awesome learning opportunity...

by Gad A

Apr 16, 2019

Excellent course, it provided insights into large sets of Data and their structuring, which had not been explored before.


Apr 15, 2019

Good Course

by Maximilian P

Apr 11, 2019

The topics covered in the course were very interesting, though the course would have been more valuable if accompanied with python programming of case studies.

Kind regards Max

by marco m

Apr 08, 2019

I recommend this course !! Good support's material, speaking and methods.

by Bart v D

Apr 04, 2019

Very well explained, provides a good basic understanding of the topic process mining.

by Glenda

Mar 27, 2019

Very good, very thorough course - especially because of the many exercises strewn across the videos. The subject matter is not trivial - I often feel the need to re-read material in the accompanying text book, and it's taking me many weeks to find the time to complete this course. The videos, in my view, are too long. This means that there should probably be twice as many videos (they should not be longer than 10 min), or some of the material ought to be left out - mostly foundational stuff. Doing that would, however, invalidate the course as a stand alone introduction - I give full stars because it is in my experience, really hard to pull all of this off. Could only be done by a true expert like van der Aalst.

by Brigitte V

Mar 20, 2019

Very clear cource and with also learning by using real cases

by Viktoriia

Mar 05, 2019

I think practical tasks in ProM should be included

by Maros K

Feb 15, 2019

Great course, it covers basics of process mining, from petri net, over pm algoritms to steps how to do process mining on real data.

by Klim

Feb 12, 2019

The course material was very well explained during the lectures. The course gave a very good overview of the PM field and its practical applications.

by An N

Feb 06, 2019

The course is a very nice introduction. I would have liked to give more additional hints to more advanced methods for an audience interested in perusing a PhD in this field. E.g. some optional implementation tasks/project would have been nice.

by sharath

Feb 05, 2019

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

by Niko M

Feb 04, 2019

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

by Ahmed E

Feb 03, 2019

it's amazing <3

by Alexey 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 Davide D

Jan 18, 2019

Perfectly fit my expectations.