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Avis et commentaires pour d'étudiants pour Exploitation des processus : la science des données en action par Université technique d'Eindhoven

4.7
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1,008 évaluations
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264 avis

À propos du cours

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....

Meilleurs avis

RE
1 juil. 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.

PP
9 déc. 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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251 - 263 sur 263 Avis pour Exploitation des processus : la science des données en action

par Sylvie B

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12 janv. 2020

Significant learnings on content which appears rationale but proved not to be in modules 2 and 4. The concepts of deadlock, soundness, live is not well explained. The time required is significantly more than advertized by Coursera. The quiz are not timed, even though there is a time indication. Some questions on the quiz have multiple correct answers, which are sometimes very subjective and tricky to get right. Lots of mental gymnastics and computation. Sadly, the concept of peer-graded assignment to strive for honors roll does not work well as the number of learners at anytime is very few, if any. The honors assignment on module 4 is very lengthy and regrettably cannot be completed satisfactorily because of software issues.

par alex b

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20 oct. 2018

Some topics are a bit glazed over and others with concepts that are acknowledged to have major shortcomings (e.g. the alpha algorithm) have a heavy focus in the course and exam despite these shortcomings. Frequent notational switches ("we can automatically change this to ___ ") can make some lectures harder to follow as well, if you're not perfectly versed in some of the leveraged notations in this course. OK overall.

par Martin S

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4 déc. 2018

Good introduction to theory of process mining, but most of the techniques are problematic and therefore not practical, and the test questions are tedious as they focus on testing whether you can remember the theory rather than how to apply the theory to real-world problems.

par Niko M

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4 févr. 2019

Very good course. More real life cases and process mining examples would be beneficial.

par Babak N R

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14 août 2021

It should be more practical and the screenshots quality needs to be improved

par Daniel C J

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12 juil. 2021

Poco enfocado al soundness

par 桃花岛主

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30 juil. 2017

Some difficult issues need to explain in detail and more patiently

par Vicente C

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18 oct. 2020

too much focus on theory i can't apply this to any real data

par Sinkov E

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26 janv. 2021

The whole course was duplicating a book of an author. It reminds listening to an audiobook. The introduction looked very appealing, so I immediately bought it. It is the first course when I can state that it was a complete waste of time and effort. In my opinion, the teaching skills of an author are fairly low. The material is supplied in a linear manner and honestly, after watching lectures almost no information stays. I've tried watching additional videos via youtube and even those unprofessional free videos are more helpful.

par Ashmin

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30 juil. 2017

Week 2,3 and 4 need more focus in terms of explanation as it was very difficult to answer questions based on what the tutor taught. The tutor can be more illustrative with respect to the concepts taught in these weeks.

par Prashant R

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21 juin 2020

The course is very unstructured, and requires certain level of mathematical concepts in Data Mining.

The are many instances where clarity of instruction is missing.

Overall quality of the course can be improved.

par Jay L

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12 sept. 2018

Poorly constructed and difficult to learn from. this class.

par Jeff B

•
7 mai 2020

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