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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

4.8
581件の評価
148件のレビュー

コースについて

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....

人気のレビュー

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.

AT

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!

フィルター:

Process Mining: Data science in Action: 1 - 25 / 145 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 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 Janid A

Dec 11, 2018

The course is excellent, clear and simple and can bring improvements in many applied fields

by Helena F L

Nov 25, 2018

Great course.

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 Brigitte V

Mar 20, 2019

Very clear cource and with also learning by using real cases

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 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 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 Davide D

Jan 18, 2019

Perfectly fit my expectations.

by Ahmed E

Feb 03, 2019

it's amazing <3

by Bart v D

Apr 04, 2019

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

by marco m

Apr 08, 2019

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

by Francisco I S R

Dec 22, 2018

Great course!

by Christos H

Jan 06, 2019

Very informative course.

by Alexander F P L

Dec 09, 2018

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

by Arash D S

Sep 21, 2018

This course was fantastic and I learn a lot of new ideas about data and understanding of data.

by Behrouz S

Oct 28, 2018

Thank you Prof. Aalst

Thank you coursera

by Mahendra V

Jul 22, 2018

Well explained, Knowledge oriented..

by Uladzislau L

Sep 30, 2018

Very well structured course with good connection between lectures and excercises.

by Yuvaraj

Sep 18, 2018

helpful

by Vishnu D S

Aug 24, 2018

Good Learning and very well designed

by Ahmed T

Jan 22, 2017

Amazing Course :)

by Yosef A W

Jan 21, 2018

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