Chevron Left
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

4.7
звезд
Оценки: 935
•
Рецензии: 241

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

RE
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.

PP
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.

Фильтр по:

76–100 из 238 отзывов о курсе Анализ процессов: обработка и анализ данных в действии

автор: Bart V d W

•
26 янв. 2018 г.

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

автор: Maros K

•
15 февр. 2019 г.

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

автор: Caio C d V

•
12 нояб. 2017 г.

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!!!

автор: Lu Z

•
10 сент. 2019 г.

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

автор: Mariano A M

•
24 июля 2017 г.

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

автор: Gad S A

•
16 апр. 2019 г.

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

автор: Abdulrahman A T

•
25 июня 2020 г.

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

автор: Kerim A

•
18 апр. 2019 г.

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

автор: Jean-Paul d V

•
3 мая 2021 г.

Packed with valuable information and reviews of tools that are easy to access and experiment with. Highly recommended!

автор: towerb

•
20 дек. 2020 г.

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

автор: Sergei M

•
15 сент. 2017 г.

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

Thanks!

автор: Marcela G

•
16 нояб. 2016 г.

Excellent course about Process Mining, it's explained all meant to understand process discovery with Data analysis.

автор: Frank G

•
15 янв. 2017 г.

这门课程十分理论知识丰富，又贴近实际应用，很棒。This course is really fantastic, it both has wonderful academical and practical knowledge.

автор: Yuting H

•
23 янв. 2020 г.

It's a great and well-structured course that I can gain fundamental knowledge of process mining quickly. Thanks!

автор: Gerard H

•
30 мая 2020 г.

Very informative and thorough course about process mining. I will definitely make use of those skills learned.

автор: Greg L

•
4 янв. 2017 г.

very comprehensive. well structured. good pace, I would recommend having the book for reference/research.

автор: Carlos D

•
22 авг. 2019 г.

Outstanding!. Very well structured, The questions inside lectures really help you to get into the topic

автор: Alix C

•
29 мар. 2018 г.

Easy to understand and very comprehensive. Examples are challenging but help to understand everything.

автор: Jorge T

•
21 мар. 2021 г.

Very insightful learning about Process Mining with interesting exercises to consolidate the knowledge

автор: Marcin N

•
2 февр. 2020 г.

Great introduction into process mining. Excellent examples illustrating the theory. Time well spent!

автор: Ahmed B K

•
16 авг. 2020 г.

Good introduction for process mining tool , thanks to everyone who has contributed to this course !

автор: Szedelényi J

•
2 июня 2017 г.

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

автор: Yahya P

•
23 сент. 2020 г.

Great course. very inspiring. Makes me want to take phd program that is involving process mining.

автор: Philip S

•
9 дек. 2019 г.

Very useful course for all data analytics fans that want to know how process mining tools work.

автор: Arash D S

•
21 сент. 2018 г.

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

Coursera Footer

Start or advance your career

Browse popular topics

Upskill your team

Earn a degree or certificate online

Coursera

Сообщество

Еще

Учитесь, где угодно
Загрузить из App StoreЗагрузить в Google Play
Placeholder