The virtual course "Process Mining: Data Science in Action - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 22 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Process mining is the missing link between model-based process analysis and data-driven analysis techniques.
Through concrete data sets and easy-to-use software, the course provides data science knowledge that can be directly applied to analyze and improve processes in a variety of domains.
Data science is the profession of the future, because organizations that cannot use (big) data intelligently will not survive.
It is not enough to focus on data storage and analysis.
The data scientist also needs to relate data to process analysis.
Process mining bridges the gap between traditional model-based process analysis (for example, simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.
Process mining seeks to match event data (ie, observed behavior) with process models (handmade or automatically discovered).
This technology has only recently been available, but 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 company, understanding the browsing behavior of customers using the reservation site, analyzing failures in a baggage handling system, and improve the user interface of an X-ray machine.
All these applications have in common that the dynamic behavior must be related to the process models.
Therefore, 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.
Several other process analysis techniques using event data will be presented.
Additionally, the course will provide easy-to-use software, real-life datasets, and practical skills to directly apply theory in a variety of application domains.
This course begins with an overview of approaches and technologies that use event data to support decision making and the (re)design of business processes.
The course then focuses on process mining as a bridge between data mining and business process modeling.
The course is introductory level with several practical tasks.
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 prior information.
An example is the Alpha algorithm which takes an event log and produces a process model (a Petri net) that explains the behavior recorded in the log.
2.
The second type of process mining is conformance.
Here, an existing process model is compared to an event log from the same process.
Conformance checking can be used to check whether reality, as recorded in the registry, fits 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.
While conformance checking measures the alignment between the model and reality, this third type of process mining aims to change or extend the model a priori.
An example is the extension of a process model with performance information, for example by showing bottlenecks.
Process mining techniques can be used offline, but also online.
The latter is known as operational support.
An example is the detection of the nonconformity at the time the deviation actually occurs.
Another example is time prediction for running cases, that is, given a partially executed case, the remaining processing time is estimated based on historical information from similar cases.
Process mining provides not only a bridge between data mining and business process management; it also helps address the classic divide between "business" and "IT."
Evidence-based business process management based on process mining helps create common ground for business process improvement and information system development.
The course uses many examples that use real life event logs to illustrate the concepts and algorithms.
After taking this course, one can run process mining projects and have a good understanding of the field of Business Process Intelligence.
After completing this course, you should: - have a good understanding of Business Process Intelligence techniques (particularly process mining), - understand the role of Big Data in today's society, - be able to relate the techniques of process mining with other analysis techniques, such as simulation.
, business intelligence, data mining, machine learning and verification,
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Eindhoven Technical University
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