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Вернуться к Анализ процессов: обработка и анализ данных в действии

Отзывы учащихся о курсе Анализ процессов: обработка и анализ данных в действии от партнера Технический университет Эйндховена

Оценки: 916
Рецензии: 238

О курсе

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....

Лучшие рецензии

1 июля 2019 г.

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 дек. 2019 г.

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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26–50 из 235 отзывов о курсе Анализ процессов: обработка и анализ данных в действии

автор: Paulien L

10 июня 2019 г.

I'm a novice to data science and took this course after an (offline) post graduate education Big Data Analyst. I learned about Disco during that training. With this Coursera-course I wanted to know more in detail about procesmining.

Though it was quite jejune and theoretical sometimes I found it interesting and doable enough. With the exams, practising and assignment alltogether I feel it did come to live as well. So I made it to the end and feel happy and proud to complete this course. Many thanks to the team om TU/e!

автор: jhonatan c c g

3 авг. 2019 г.

The course accomplish with its own commitment as introductory level for this useful growing tendency for process analysis using datadriven with various practical assignments, welldone explained by the professor and easy for understand from simple examples until the one´s more difficult . i liked a lot that you can use software and make simulations with real data, besides excellent complement with its book where you can go in deep about topics .

автор: Rinus F

19 дек. 2019 г.

This course introduces the concept and basic principles of process mining extremely well to the user. It stimulates you to go looking for new analyses to improve business processes and gets you thinking about potential applications in your work space.

The course is simple enough for beginners, yet gives enough detail to be able to start implementation of process mining.

автор: Natanael Y W

25 дек. 2020 г.

This course provides a gentle introduction to process mining. The content was easy to grasp. Difficult parts of the course were explained succinctly and thus could shorten the learning curve. I really recommend this course as a starter for those who want to take part in the process mining community either as a researcher, business professional, etc.

автор: João D V

7 авг. 2019 г.

This has been one of my favourite courses in Coursera. I thought it was very well organized and I greatly appreciated the attention that was given to using the tools. I also thought the quizz and assignments allowed me to identify where I needed to put more effort and review the learning material. Overall great experience!

автор: dstart

28 июля 2018 г.

Very interesting topic and the course is beautifully designed. The different techniques are described in the fitting amount of detail and many examples of process mining in practice are given. Plus the two process mining tools are shown and explained so that it is possible to use them in one's one projects.

автор: Rodrigo C

1 апр. 2018 г.

This course is very useful. Its content give us a clear notion of process mining and how to apply it to discover the process model.

It helped me identifying real cases bottlenecks in my own process and my analysis are more data-based. This chance in my approach made my work more reliable and "to the point".

автор: simofura

20 сент. 2016 г.

Great great course.

I'm a beginner in this matter so to me there are cases difficult to understand. One thing that could help a lot would be more exemplas of real life for each theorical concept. As done at the beginning with coffee, latte, muffin, ect.

Thanks for the energy dedicated to create this course.

автор: 李正锋

20 февр. 2021 г.

I'm a postgraduate student from China, cost about 2 months nearly I have finished and learnt all the courses, I do learn enormously from these lectures. In my opinions, our country has not used the process mining, I can't find any about it almostly. I expect the future about the process mning technology.

автор: Michelle T

24 апр. 2018 г.

This is a very good course for those who are interested in process mining. I continue to review and improve my understanding on each concept, and one day I will be able to reap the fruit of all the process improvements through applying this in work place. Thank you very much for offering this course!

автор: Robin C

16 авг. 2020 г.

This is a brilliant course, led by a world authority on process mining who freely imparts his knowledge and presentation skills. I cannot critic it.

From a personal point of view I have learnt so much and the access to the tools makes it so tangible and valuable.

A big and sincere THANK YOU!!!

автор: M. C

7 мар. 2021 г.

One of the most interesting course I have ever attendend.

Professor Wil Van der Aalst is outstanding at explaining the different topics and elements of the course.

There are also some lectures and tutorials for Disco and ProM beninners, that were also very useful.

Thank you!

автор: nsoltani

9 нояб. 2020 г.

It was one of the best courses I've ever passed. A well-designed course with great content and quizzes. Moreover, the professor talks fluently and clearly and made the topics easy to understand. I want to say thank you to both course instructor prof. Wil and Coursera.

автор: Aakashkumar

29 апр. 2020 г.

In some up, I can say it's the best certificate course I have done. Everything is so well organized & planned curriculum that if you are looking forward to learning something new in this modern era then just go for it. You will not regret, mark my word if you want to!

автор: EUN D N

12 апр. 2020 г.

This is an eye-opening course providing a different aspect of process analysis. After completing the course, I fully understand the concept of process discovery, conformance and enhancement, which is a core part of the business process in our business operations.

автор: Ebrahim S

28 окт. 2020 г.

This has, by far, been the most comprehensive course that I have seen anybody instruct online. Astonishing how this level of information can be conveyed through one-way courses such as this. You'll get to get a good sense of the Dutch accent as well ;)

автор: Xavier B

15 нояб. 2020 г.

Clear, easy to understand, focused on cases, uses parallel pictures to describe concepts (like maps/process mining), never boring but a lot of information to acquire and a different mindset to develop. Great course, thanks a lot, happy to have succeed

автор: Matthias A

13 февр. 2021 г.

Excellent course! It gives a very good introduction to process mining, a rather new data science discipline that is not yet used in many companies, and therefore has a great potential in the future. So attending this course is highly recommendable.

автор: Rony S

19 авг. 2017 г.

In depth course for process mining. Anyone trying to jump into a career on Business processes, or wants to apply data science to business processes, should take this course. It is more involved than other Data Science course, so give it your all.

автор: AHOSSI A B

3 июля 2019 г.

Excellent cours, pour peu que l'on ait une fois suivi un cours de data mining, on voit très vite une chance de se spécialiser. De même que pour un business process analyste, il en ressort une nette opportunité d'étendre son champ d'expertise.

автор: John R

13 нояб. 2016 г.

Awesome Course, great lectures, the data that is available to use for ProM and Disco really made the difference. I would recommend this course for anyone interested in process analytics or Lean/ Six Sigma business process optimization.

автор: Matthew

1 авг. 2016 г.

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.

автор: Kirill D

28 янв. 2018 г.

Great course! Well balanced theoretical information and practical exercises. Algorythms were explained in easy for understanding way. Thank you very much, Wil van der Aalst, Joos Buijs, and the rest of the Process Mining team!

автор: Balázs H

8 мар. 2018 г.

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.

автор: Simin M

28 дек. 2020 г.

This course was extremely well-organized and well-presented by the best teacher ever. The quizzes and assignments were designed perfectly so that the most important parts discussed in lectures could stick in mind.