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mooc:processmining [2017/10/20 10:02]
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mooc:processmining [2017/10/20 10:09]
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-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. ​+[[https://​www.coursera.org/​learn/​process-mining/|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. ​
  
-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"​. ​+===== About the course ===== 
  
-[[https://​www.coursera.org/​learn/​process-mining/|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. ​+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"​. ​
  
 [[https://​www.coursera.org/​learn/​process-mining/​|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. ​ [[https://​www.coursera.org/​learn/​process-mining/​|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. ​
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