[AISWorld] MOOC “Process Mining: Data science in Action” to be repeated as of April 2015
j.c.a.m.buijs at tue.nl
Fri Mar 13 07:34:30 EDT 2015
Due to the large success of the first run, this 6 week online course is repeated as of April. The course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.
Starts: April 1st, 2015
For more information and to register visit:
Process Mining: Data science in Action<https://www.coursera.org/course/procmin>.
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).
Process mining 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 a 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 Coursera course “Process Mining: Data science in Action” explains the key analysis techniques in process mining. Over 40,000 participants joined in the first run where they learned 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 were also presented. Moreover, the course provides easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. To give everyone who missed the first run a chance to follow this course, the course runs again as of April 1st, 2015.
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