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아이트호벤 공과 대학의 Process Mining: Data science in Action 학습자 리뷰 및 피드백

4.8
599개의 평가
150개의 리뷰

강좌 소개

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....

최상위 리뷰

RK

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.

EC

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.

필터링 기준:

Process Mining: Data science in Action의 147개 리뷰 중 101~125

교육 기관: Kerim A

Apr 18, 2019

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

교육 기관: Mohibullah K

May 15, 2019

Very practical oriented course on Process Mining.

교육 기관: Pasqualino D N

Jul 26, 2019

Very useful course. Well done and very clear.

교육 기관: jhonatan c c g

Aug 03, 2019

The course accomplish with its own commitment as introductory level for this useful growing tendency for process analysis using datadriven with various practical assignments, welldone explained by the professor and easy for understand from simple examples until the one´s more difficult . i liked a lot that you can use software and make simulations with real data, besides excellent complement with its book where you can go in deep about topics .

교육 기관: João D V

Aug 07, 2019

This has been one of my favourite courses in Coursera. I thought it was very well organized and I greatly appreciated the attention that was given to using the tools. I also thought the quizz and assignments allowed me to identify where I needed to put more effort and review the learning material. Overall great experience!

교육 기관: Mustafa G

Jun 01, 2019

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

교육 기관: Stephen v G

Aug 18, 2019

Complex material, but presented in an understandable way. Assignment was practical. Good integration with open source software packages.

교육 기관: Carlos D

Aug 22, 2019

Outstanding!. Very well structured, The questions inside lectures really help you to get into the topic

교육 기관: 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.

교육 기관: AHOSSI A B

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.

교육 기관: 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.

교육 기관: Челушкин А А

Aug 31, 2019

Отличный курс, очень понятно и грамотно рассказывается о process mining

교육 기관: Marcin M

Sep 01, 2019

Very interesting course and well done. Descriptions, presentation, and slides are clear. I would for sure search for more courses in this field.

교육 기관: Lu Z

Sep 10, 2019

Very high-quality course. It is an intermediate level course, so expect some difficulty learning this. But it totally worth it.

교육 기관: 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!

교육 기관: mcy

Sep 26, 2019

REALLY NICE! MUCH APPRECIATE!

교육 기관: paulo

Sep 30, 2019

Excelent course!

교육 기관: Martin B

Dec 10, 2018

There should be a mandatory data science Project to make the students experience the practical side process mining projects

교육 기관: Somayeh M

Dec 31, 2018

Thanks to Prof. Wil Van Der Aalst and his team for providing me with the opportunity of learning process mining. That was terrific!

교육 기관: 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.

교육 기관: 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

교육 기관: UMAIR P

Apr 15, 2019

Good Course

교육 기관: Viktoriia

Mar 05, 2019

I think practical tasks in ProM should be included

교육 기관: sharath

Feb 05, 2019

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

교육 기관: Sameer K

Aug 08, 2018

Good introductory course to data mining. It would help if the disco demo version has a higher limit ( >100 lines) as that would allow better experimentation with real data.