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.
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.
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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.
4-6 hours per week
Part time/ Full time:
Course start date –28-Nov-2016
A basic understanding of logic, sets, and statistics (at the undergraduate level) is assumed. Basic computer skills are required to use the software provided with the course (but no programming experience is needed). Participants are also expected to have an interest in process modeling and data mining but no specific prior knowledge is assumed as these concepts are introduced in the course.
Wil van der Aalst: – Eindhoven University of Technology