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Voltar para Mineração de processos: ciência de dados na prática

Comentários e feedback de alunos de Mineração de processos: ciência de dados na prática da instituição Universidade Tecnológica de Eindhoven

1,077 classificações

Sobre o curso

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....

Melhores avaliações


1 de jul de 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.


9 de dez de 2019

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 — 175 de 279 Avaliações para o Mineração de processos: ciência de dados na prática

por Giovanni Q

21 de set de 2021

Absolutely recommended! A MUST about process mining!!

por Maroua N

23 de dez de 2021

one of the richest and difficult courses on coursera

por Dhanyesh R

24 de jun de 2022

Complete Eye opener on process mining applications.

por stephane d

5 de jan de 2022

Great course. Thanks a lot Professor Van der Aalst.

por Hugues

6 de jul de 2021

Trés instructifs sur les méthodes de Process Mining

por Mohammad R H N

30 de mai de 2018

This course was very applicable and helpful for me.

por Yoon P

24 de abr de 2016

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

por Julio C S G

25 de jun de 2022

El mejor curso para mi especialidad, mucho aporte!

por Mohibullah K

15 de mai de 2019

Very practical oriented course on Process Mining.

por xing w

4 de jun de 2016

A comprehensive introduction to process mining!

por Pasqualino D N

26 de jul de 2019

Very useful course. Well done and very clear.

por Rob B

10 de out de 2016

Great course and very nice video's lectures!

por Cristiano F

29 de abr de 2017

I learnt a lot from this course. Excellent!

por Larissa H

12 de abr de 2020

Great intro to the data science world ;)))

por Djana R

24 de jun de 2018

Interesting course. I like it.Recommended.

por Харькина Е А

29 de ago de 2021

It was a very complex and amazing course!


28 de jan de 2021

A must to get familar with Process Mining

por Mohammad H E

8 de dez de 2019

Thanks to Wil van der Aalst.

por Behrouz S

28 de out de 2018

Thank you Prof. Aalst

Thank you coursera

por abdelahmid M r

28 de ago de 2020

comperhensive course in Process mining

por Braulio B

16 de nov de 2020

Great! Very clear and very practical

por Thibaut L

21 de jan de 2020

An in depth course on Process Mining

por Vishnu D S

24 de ago de 2018

Good Learning and very well designed

por Mahe V

22 de jul de 2018

Well explained, Knowledge oriented..

por Hamed R

10 de jul de 2022

The best course in Process Mining !