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news:mooc_process_mining_data_science_in_action_to_be_repeated_as_of_october_2015 [2015/09/14 13:04]
hverbeek [MOOC “Process Mining Data science in Action” to be repeated as of October 2015]
news:mooc_process_mining_data_science_in_action_to_be_repeated_as_of_october_2015 [2015/09/28 11:25] (current)
hverbeek [MOOC “Process Mining Data science in Action” to be repeated as of October 2015]
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 **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). ​ **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). ​
  
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 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"​. 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"​.