Course content

This course introduces Sustainability Analytics (SA), a new interdisciplinary research field, which aims to offer data-driven decision support for sustainable development of operations in private and public sectors. The digital transformation with its changes through combination of information, computing, communication and connectivity technologies, offers new possibilities for developing data-driven machine learning solutions to support the creation of decision support systems and guide the industry in a more sustainable direction.

This course is interdisciplinary, combining data science, sustainability science, and management science.

Learning outcome

On satisfactory completion of this course, you should be able to:

1. Define and explain central concepts, technologies and methods proposed for sustainability analytics
2. Define key components of sustainability analytics decision support systems
3. Describe and apply systems engineering in developing decision support systems
4. Describe key challenges and concerns related to decision support system development
5. Analyze sustainability challenges using key analytics methods, tools and approaches
6. Plan, design, implement and test a sustainability analytics decision support system for organizational problem solving

Learning methods and activities

Lectures, self-study, pen-and-paper exercises, computer exercises, and project work.

Further on evaluation

There will be no re-sit exam in this course. If students want to improve their grade on the next ordinary exam, they only need to submit the final report.

Specific conditions

Course materials

Course literature will be announced at the start of the course.