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Process Mining: Data science in Action

4.7
145 reviews

Created by: Wil van der Aalst

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Overall Score : 94 / 100

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Course Description

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.
Read More product length iconTime: 30h

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Instructor Details

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Distinguished university professor Wil van der Aalst is one of the most cited computer scientists in the world and thought leader in areas such as business process management, process mining, and data science. He is scientific director of the Data Science Center Eindhoven (DSC/e) and chair of the AIS group at Eindhoven University of Technology. He also has (honorary) appointments at other universities such as Queensland University of Technology (Australia), the National Research University, Higher School of Economics in Moscow (Russia), and Tsinghua University (China).

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Reviews

4.7

145 reviews in total

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By Hemant K on 30-Oct-19

awesome

By Marlene S on 25-Oct-19

Excellent material and instructor! Thank you for sharing!

By paulo on 30-Sep-19

Excelent course!

By mcy on 26-Sep-19

REALLY NICE! MUCH APPRECIATE！

By Lu Z on 10-Sep-19

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

By Marcin M on 1-Sep-19

Very interesting course and well done. Descriptions, presentation, and slides are clear. I would for sure search for more courses in this field.

By Carlos D on 22-Aug-19

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

By Stephen v G on 18-Aug-19

Complex material, but presented in an understandable way. Assignment was practical. Good integration with open source software packages.

By João D V on 7-Aug-19

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!

By jhonatan c c g on 3-Aug-19

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 .

By Pasqualino D N on 26-Jul-19

Very useful course. Well done and very clear.

By Radu-Andrei C on 13-Jul-19

I am surprised to have learned so many new topics and methods for data science in one course. It's like opening a pack of trading cards (e.g. Pokemon TCG or Yu-Gi-Oh cards) and finding that you don't have any duplicates. I think the knowledge in this course is a great addition to the skill set of any data scientist (regardless if you currently work or not with processes). Finally kudos to Prof. van der Aalst and his team. Very well planned lectures, quality content and no boring quizzes. I think it would be good for the future to add more quiz variations, as taking a quiz twice to improve one's answer one will work with exactly the same questions and numbers. I would also like to see a future Process Mining course, with more in-depth lectures on the topics of conformance checking and enhancement. A more practical side would also be welcome, for example coding some algorithms.

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Process Mining: Data science in Action
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  • Video Duration: icon 30h
  • Price: Free
  • Provider: icon Coursera
  • University: icon Eindhoven University of Technology
  • Language: icon English
  • Certificate: icon Yes

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