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返回到 过程挖掘:数据科学实战

學生對 埃因霍温科技大学 提供的 过程挖掘:数据科学实战 的評價和反饋

4.7
1,058 個評分
274 條評論

課程概述

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....

熱門審閱

RK

2019年7月1日

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

2019年12月9日

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.

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151 - 过程挖掘:数据科学实战 的 175 個評論(共 274 個)

創建者 Giovanni Q

2021年9月21日

Absolutely recommended! A MUST about process mining!!

創建者 Maroua N

2021年12月23日

one of the richest and difficult courses on coursera

創建者 stephane d

2022年1月5日

Great course. Thanks a lot Professor Van der Aalst.

創建者 Hugues

2021年7月6日

Trés instructifs sur les méthodes de Process Mining

創建者 Mohammad R H N

2018年5月30日

This course was very applicable and helpful for me.

創建者 Yoon P

2016年4月24日

Wow! Changing my life and career, this course does.

創建者 Mohibullah K

2019年5月15日

Very practical oriented course on Process Mining.

創建者 xing w

2016年6月4日

A comprehensive introduction to process mining!

創建者 Pasqualino D N

2019年7月26日

Very useful course. Well done and very clear.

創建者 Rob B

2016年10月10日

Great course and very nice video's lectures!

創建者 Cristiano F

2017年4月29日

I learnt a lot from this course. Excellent!

創建者 Larissa H

2020年4月12日

Great intro to the data science world ;)))

創建者 Djana R

2018年6月24日

Interesting course. I like it.Recommended.

創建者 Харькина Е А

2021年8月29日

It was a very complex and amazing course!

創建者 EVEILLEAU

2021年1月28日

A must to get familar with Process Mining

創建者 Mohammad H E

2019年12月8日

Thanks to Prof.dr.ir. Wil van der Aalst.

創建者 Behrouz S

2018年10月28日

Thank you Prof. Aalst

Thank you coursera

創建者 abdelahmid M r

2020年8月28日

comperhensive course in Process mining

創建者 Braulio B

2020年11月16日

Great! Very clear and very practical

創建者 Thibaut L

2020年1月21日

An in depth course on Process Mining

創建者 Vishnu D S

2018年8月24日

Good Learning and very well designed

創建者 Mahe V

2018年7月22日

Well explained, Knowledge oriented..

創建者 MANUEL H G M

2022年3月18日

I learn multiple interesting things

創建者 Nikita

2018年6月4日

Thanks a lot. It was very usefull!

創建者 Sinan B

2021年1月10日

Comprehensive and very rewarding!