This course builds on concepts taught in the Leiden courses on Data Mining and Databases.
It consists of two parts: Data Science and Process Modelling.
Data Science. The data science 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 science.
For this part of the course, students join the 6 ECTS course on Data Science by dr. Suzan Verberne, course code 4032DASC6.
Process Modelling. 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, data science 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 data science (as specified in course 4032DASC6) 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 data science and/or process mining.
The most updated version of the timetables can be found on the students' website:
Mode of instruction
Weekly lectures and lab sessions.
Exam (60%) and practicum (40%).
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
See Data Science (4032DASC6).
The following book is optional reading material for the process modelling part of the course.
- Wil van der Aalst, Process Mining: Data Science in Action, 2nd edition, Springer, 2016.
Onderwijscoördinator Informatica, Riet Derogee.