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Data Science en Process Modelling


Admission requirements

Not applicable


This course builds on concepts taught in the Leiden courses ong Data Mining and Databases.

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 analytics. Each of these topics is addressed specifically in a business-oriented and/or economics 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, data science and process modelling finally come together in the topic of process mining: a data-driven approach to understanding business process management.

Course objectives

By the end of the course, the student should be able to:

  1. Be aware of the most relevant concepts related to data science and process modelling
  2. Be able to model a business process in a common notation and
  3. Understand the differences, strengths, and weaknesses of specific modeling languages.
  4. Be able to undertake an independent research project into data science and/or process mining.
  5. Be able to apply and understand data science methods in an organizational context


The most updated version of the timetables can be found on the students' website:

Mode of instruction

Weekly lectures and lab sessions (both online when needed).

Assessment method

Online oral exam (60%) and practicum (40%).

Reading list

The following book is compulsary for the course:

  • Wil van der Aalst, Process Mining: Data Science in Action, 2nd edition, Springer, 2016.
    ISBN: 9783662498507

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.


You can enrol via uSis . More information about signing up for classes and exams can be found here .

Contact information

Onderwijscoördinator Informatica, Riet Derogee.


Data Science & Process Modeling.