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Process Mining

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.

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Rating 4.6 based on 186 ratings
Length 7 weeks
Effort 6 weeks of study, 3 to 5 hours/week of material + self study
Starts Nov 21 (7 days ago)
Cost $49
From Eindhoven University of Technology via Coursera
Instructor Wil van der Aalst
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Business
Tags Data Science Data Analysis Business Business Essentials

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What people are saying

process mining

Very informative lectures to get a perspective of process mining.

Sets you on a path where you can learn more Process Mining on your own using free internet resources and experimentation.

Very good course Great course about general principles of process mining!

This is a very good course for those who are interested in process mining.

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.

Great introduction to Process mining with practical applications incl 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.

Very interesting as an introduction to Process Mining.

I believe the course laid the right foundation to understand the functioning of process mining software such as Disco and ProM.

I really liked how the complex nature of Process Mining is explained with examples.Both theoretical and practical sides of Process Mining are explained.References to more specialized and advanced materials were given so that one can further research for particular needs.Great work Wil!I would really enjoy to see a course like "Comparative Process Mining" or "Advanced Practical Process Mining Applied" from you.

I would like to have 1 more week in which one can go through a process mining process from start to end, step by step.

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

Easy-to-understand with useful examples, and also process mining is a technique that is applicable to many cases.

a new field which could help any one to find a better position at work and it will help in performing the most common process mining activities.

furthermore the course will slightly helps to conduct a process mining project.

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wil van der aalst

Thank you very much, Wil van der Aalst, Joos Buijs, and the rest of the Process Mining team!

Many thanks to Wil van der Aalst and to everyone who supported to bring this course.

Professor Wil van der Aalst is truly a Guru in the field and his team must be complemented for conducting such a useful course on Coursera.

Wil van der Aalst.

Thanks to Prof. Wil Van Der Aalst and his team for providing me with the opportunity of learning process mining.

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very well

Very well thought and laid out course.

Very beautifully done: information very well and clearly organized, illustrated, presented, and referenced.

The pictures are not very well to watch.

Very well structured, The questions inside lectures really help you to get into the topic Complex material, but presented in an understandable way.

I thought it was very well organized and I greatly appreciated the attention that was given to using the tools.

Very well planned lectures, quality content and no boring quizzes.

Very well explained, provides a good basic understanding of the topic process mining.

The course material was very well explained during the lectures.

Very well structured course with good connection between lectures and excercises.

Good Learning and very well designed Good introductory course to data mining.

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very interesting

Very interesting course, explained in a understandable way and rich of high level topics.

Very interesting course.

This course is very interesting!

Very interesting!!!

My conclusion is that process-mining is a very interesting field where very cool algorithms can be applied but in the end the work is boring and tedious and the tools that we have available today are not fun to use and full of bugs.

Very interesting course and well done.

Very interesting topic and the course is beautifully designed.

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data science

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.

Process Mining a fine complement to the more typical data science coursework.

I found the course content to be hugely meaningful in enhancing my learning of how data science tools used in Process Mining can meaningfully help solve real world problems.

I am surprised to have learned so many new topics and methods for data science in one course.

I'm a novice to data science and took this course after an (offline) post graduate education Big Data Analyst.

If you are like me and want to add another layer on top of the data mining/data science knowledge and have some business ambitions, I would definitely recommend the course to you!

The course is excellent, clear and simple and can bring improvements in many applied fields There should be a mandatory data science Project to make the students experience the practical side process mining projects Really good course, I could apply the knowledge I acquired direclty for my job.

Process Mining gave me insights into an area of Data Science, which I think is largely neglected - 'Process Optimization'.

This course provides the foundation to treat 'Process' at the same level of 'Data' when it comes to Data Science.

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recommend this course

I would recommend this course for any one who is interested to know more about process optimization and discovery.

I would recommend this course for anyone interested in process analytics or Lean/ Six Sigma business process optimization.

Good Course The topics covered in the course were very interesting, though the course would have been more valuable if accompanied with python programming of case studies.Kind regards Max I recommend this course !!

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Coal Mining $71k

Mining Sales Specialist Manager $72k

Supervisor Mining Engineer $87k

Mining Operator $87k

Mining Account Manger $92k

Mining Specialist $94k

Product support Sales Mining $95k

Mining Division - Client Advisor $96k

Mining Specialist Engineering $108k

General Foreman/Mining Engineer $111k

Data Mining $118k

Mining Engineer Manager $123k

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Rating 4.6 based on 186 ratings
Length 7 weeks
Effort 6 weeks of study, 3 to 5 hours/week of material + self study
Starts Nov 21 (7 days ago)
Cost $49
From Eindhoven University of Technology via Coursera
Instructor Wil van der Aalst
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Business
Tags Data Science Data Analysis Business Business Essentials

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