This course was offered in April 2026. In the future, in can be offered in two ways:
- Instruction-led offerings: We plan to schedule the course in a synchronous learning format, guided by experienced tutors, again in the future. For information about our current offerings, please visit the Open Access Courses section of the website.
- Tailored offerings: This course can be customized to meet your personal or organizational needs, with guidance from an experienced instructor. For more information, please contact us at vis@merit.unu.edu.
About the course
This course focuses on studying sustainable development with data analysis, using methodologies and tools coming from network science, machine learning and economic complexity. The aim of the course is to provide students with practical examples of complexity analysis in specific applications, particularly in relation to sustainable development (both climate and social).
The course includes hands-on tasks and projects using Python to apply these concepts in practice.
Participants that successfully complete the course will receive a certificate validated by UNU-MERIT, including the estimated workload.
Schedule
- Course starts: 16 June
- Week 1 (16 June - 19 June):
- Lectures 1, 2 and 3 (asynchronous)
- Week 2 (22 June - 26 June):
- 22 June at 13:00-14:00 CEST: Q&A Session 1 (synchronous)
- Lectures 4 and 5 (asynchronous)
- Week 3 (29 June - 3 July):
- 29 June at 13:00-14:00 CEST: Q&A Session 2
- Students are divided in groups and prepare final assignment and presentation
- Final session: 6 July 13:00-15:00 CEST – Students present their work
Who should attend
This online course is for students who want to develop quantitative skills and learn how to use complexity analysis and network science tools to support effective policymaking.
Bachelor's and master's students are welcome to join.
Registration and Costs
Thanks to support from the Dutch Ministry of Education, Culture and Science, we are offering a limited number of tuition-free places in this virtual exchange course. However, enrolled students are expected to actively participate in the activities and submit a final group assignment.
Please apply using this online form.
Please send an email to vis@merit.unu.edu in case you have any questions.
