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Process Mining: Data science in Action(으)로 돌아가기

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

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


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.


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 76~100

교육 기관: Rami A T

Jun 21, 2017

Rich course

교육 기관: Nikolai B

May 27, 2017

This well-focused course provides both theoretical knowledge and practical skills, which could be implemented in real life of most managers (in anersons.

교육 기관: 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.

교육 기관: Vahid T

Apr 04, 2018

I love this course because it really add values to organizations by improving their bottomline

교육 기관: WangXing

Jun 04, 2016

A comprehensive introduction to process mining!

교육 기관: Cedric b

Jun 23, 2016

High quality course !

교육 기관: Matthew M

Aug 01, 2016

This course is intense and informative. The material is well-presented and the assignments have clearly benefitted a great deal of care from the instructors. Process Mining a fine complement to the more typical data science coursework.

교육 기관: Gilberto A

Oct 31, 2017

Great course, good explanation and excelente selection of topics. Totally recommended!

교육 기관: Szedelényi J

Jun 02, 2017

Guides through the fundamentals of process mining and provide hands-on skills to apply right away.

교육 기관: Jani L

Oct 19, 2016

Good balance between the more detailed technical stuff and general overview and background. Good quizzes, challenging and relevant to weekly content.

교육 기관: Tấn T M

May 24, 2017

Excellent course

교육 기관: Cristiano F

Apr 29, 2017

I learnt a lot from this course. Excellent!

교육 기관: Bart V d W

Jan 26, 2018

Very clear and thorough explanation of the important concepts of process mining, with enough room for exercises and hands-on practice

교육 기관: Balázs H

Mar 08, 2018

It was very useful and clear to understand course, I would love to have a course with deeper insight on the topic, and one which is just considering the practical use-cases separately, both based on this knowledge.

교육 기관: Djana R

Jun 24, 2018

Interesting course. I like it.Recommended.

교육 기관: Mahendra V

Jul 22, 2018

Well explained, Knowledge oriented..

교육 기관: Vishnu D S

Aug 24, 2018

Good Learning and very well designed

교육 기관: Yuvaraj

Sep 18, 2018


교육 기관: Arash D S

Sep 21, 2018

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

교육 기관: Uladzislau L

Sep 30, 2018

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

교육 기관: Behrouz S

Oct 28, 2018

Thank you Prof. Aalst

Thank you coursera

교육 기관: Gad A

Apr 16, 2019

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

교육 기관: Bart v D

Apr 04, 2019

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

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: marco m

Apr 08, 2019

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