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

4.8
별점
857개의 평가
219개의 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
2019년 7월 1일

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
2019년 12월 9일

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.

필터링 기준:

Process Mining: Data science in Action의 216개 리뷰 중 51~75

교육 기관: Andreas B

2020년 3월 10일

An excellent course on a very interesting and promising topic. Many thanks to Wil van der Aalst and his team for the great introduction to the world of process mining.

교육 기관: Pierre-Yves N

2017년 7월 16일

Excellent balance between theory, simple examples and real-life situations. Language is clear and the level of complexity appropriately defined. Two thumbs up!

교육 기관: Stefan W

2020년 5월 6일

Very in-depth and well-presented material. The quizzes are quite involved, but form a rigorous basis for testing comprehension and ability to apply material.

교육 기관: Nikolai B

2017년 5월 27일

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: Toni R

2020년 10월 14일

Very good overview of process mining. Excellent lectures and material. The assignments were very well balanced and grasped the essentials of lectures.

교육 기관: Chan J J

2020년 4월 25일

Very well planned and delivery by Prof was exceptional! I will definitely be interested to learn of more such courses from the university of Eindhoven

교육 기관: Klim M

2019년 2월 12일

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.

교육 기관: Onur D

2017년 6월 6일

I'm very glad to participate in the course. I decided to use Process Mining in my PhD thesis. Thank you Prof. van der Aalst. I hope, we meet one day.

교육 기관: Jani L

2016년 10월 19일

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

교육 기관: Anoop M

2019년 11월 27일

So much research has been done in BPM domain. This course gives a solid foundation in BPM to anyone who wishes to pursue a career in this domain.

교육 기관: Marcin M

2019년 9월 1일

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: Jason M C

2016년 6월 1일

An exceptional class that covers a very complex topic in a digestible and usable way. It's a good balance between concept and application.

교육 기관: Stephen v G

2019년 8월 18일

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

교육 기관: Tapio H

2017년 12월 12일

Enough but not too much challenge. Surprisingly not so difficult mathematically either. Difficulty between weeks could be more balanced.

교육 기관: Gelsomina C

2017년 5월 2일

This course is very interesting! A lot of things that I have learnt can be applied to all day life.

The teacher is very nice and clear!

교육 기관: Bart V d W

2018년 1월 26일

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

교육 기관: Maros K

2019년 2월 15일

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

교육 기관: Caio C d V

2017년 11월 12일

Very useful for those that are seeking knowledge about how to improve processes. I'll use it in my doctorate and also in my work!!!

교육 기관: Lu Z

2019년 9월 10일

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

교육 기관: Mariano A M

2017년 7월 24일

Very well thought and laid out course. Examples throughout the lectures clearly illustrate what the Professor wants to convey.

교육 기관: Gad S A

2019년 4월 16일

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

교육 기관: Abdulrahman A T

2020년 6월 25일

Thanks for the course. This course gives you a good introduction of both business and technical sides of process mining.

교육 기관: Kerim A

2019년 4월 18일

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

교육 기관: towerb

2020년 12월 20일

Prof. van der Aalst is a great lecturer and it is obvious that his team spent a lot of effort to create this course.

교육 기관: Sergei M

2017년 9월 15일

All my expectations were achieved. I like approach of these course, theory was not boring. A lot of practice.

Thanks!