MOOC: “Process Mining: Data science in Action” repeats in Oct 2015
Due to the large success of the first two runs, this 6 week online course is repeated in October. The course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.
First Massive Open Online Course on Process Mining
Starts: October 7, 2015
For more information and to register visit:
Process Mining: Data science in Action.
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 is not just another data mining technique.
Although both process mining and data mining start from data, data mining techniques are typically not process-centric and do not focus on event data. For data mining techniques the rows (instances) and columns (variables) can mean anything.
For process mining techniques, we assume event data where events refer to process instances and activities. Moreover, the events are ordered and we are interested in end-to-end processes rather than local patterns.End-to-end process models and concurrency are essential for process mining. Moreover, topics such as process discovery, conformance checking, and bottleneck analysis are not addressed by traditional data mining techniques and tools. 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 and provides practical tips to apply process mining immediately. Close to 70,000 participants joined in the first two runs 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 are 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 previous runs a chance to follow this course, the course runs again as of October 7, 2015.
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