The business intelligence aspects of this course deal with the ever-increasing need of organizations to analyze, visualize, mine and understand their own data. Topics include visualization, descriptive analytics and predictive analytics, but also more recent techniques such as network analytics. Each of these topics is addressed specifically in in business-oriented and/or economical context, which is reflected in the course assignments and provided case studies. The process modelling aspect of this course addresses the fact that organizations must constantly optimize, update, and monitor the execution of their processes to stay competitive and efficient. These processes are developed on the basis of organizational targets and strategic goals, but of course the underlying IT landscape is also of influence on process design, development, implementation, and execution. During this course, business intelligence and process modelling finally come together in the topic of process mining: a data-driven approach to understanding business process management.
By the end of the course, the student should be able to:
- Be aware of the most relevant concepts related to business intelligence and process modelling
- Be able to model a business process in a common notation and
- Understand the differences, strengths, and weaknesses of specific modeling languages.
- Be able to undertake an independent research project into business intelligence and/or process mining.
- Be able to apply and understand data science methods in an organizational context
For the most up-to-date schedule, see LIACS website.
Weekly lectures and seminars of 2 hours each.
Exam (60%) and practicum (40%).
The following book is compulsary for the course:
- Wil van der Aalst, Process Mining: Data Science in Action, 2nd edition, Springer, 2016.
Furthermore, the literature for the course will take the form of a collection of scientific articles that are discussed during the lectures. For the practical part we will make use of open source tools.
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More information about signing up for your classes can be found at the Faculty of Science website.
Please note that space is limited. Students who are not enrolled in the Computer Science bachelor's programme need to contact the study advisor.