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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

1,063 個評分
276 條評論


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....




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.



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.


201 - 过程挖掘:数据科学实战 的 225 個評論(共 276 個)

創建者 Tấn T M


Excellent course

創建者 吳俊儀Chunyi


The best lecture

創建者 Ahmed E


it's amazing <3

創建者 Francisco I S R


Great course!

創建者 Helena F L


Great course.

創建者 Cafer D


I like that

創建者 Rami A T


Rich course

創建者 Carlos B S


Very good.

創建者 Gustavo M


Very good!

創建者 Angeliki K


Very good!

創建者 Ignacio O E



創建者 Pablo I R P



創建者 Joonas S


Loved it!

創建者 Cleyton N d O



創建者 Hemant K



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創建者 Pavel Z



創建者 Anastasia


Thank you for your course! I really liked it, and it was a pleasure to listen to the professor and his clear explanations. It definitely made a difference to me.

I gave four stars out of five since I reckon it could have been more practice with Disco and ProM incorporated into the course. And probably it should be done for all learners, not only for those who choose to. For sure it is a quite time consuming task for the students, but maybe week quizes could be changed, so that there are both practical and theoretical parts.

Thanks again! I enjoyed it learning with you!

創建者 Mathijs L


seems that the course was updated once or twice, whereby the video is no longer up to date (example chapter of the books at the end of each course is not matching which the slides). Particular questions don't have options displayed (so you need to do a multiple-choice with all blanc answers). Very interesting course, but some chapters are hard to chew thru. Nonetheless well worked out, I am pleased with my certificate. I believe this is indeed a very promising topic for the future to be familiar with. Thank you

創建者 Elia C


Extremely interesting subject. The exposition is for the most part remarkably clear, though it amounts to a necessarily quick introduction to the vast variety of tecniques and tools. The student is supposed to conduct a lot of practice as self-study and experimenting. Giving 4 stars out of 5 instead of full marks only because I feel this course might work better in an extended format, going more in-depth on real cases and guiding the students through the hurdles and subtleties of such analyses.

創建者 Wasay A


A very information course of a new and currently developing field of process mining. Every bit of information was elaborate and clear. As an advisory associate, it had practical benefits to my own work as well. However, the only possible thing that lacked was ways to acquire the data or cleaning it to be put into the data mining software. My practical application of the concepts taught have been greatly restricted due to that.