ACE590 - Sustainability analytics and visualization

Study Period: LP1 September to November

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In this world of big data, basic data literacy is an increasingly valuable skill. This holds true in the field of sustainability, where extensive datasets are published across various sustainability topics. This course aims to give theoretical and practical knowledge in how to select, analyze, and interpret sustainability data and models, in basics of data management, and in how to extract valuable insights and build effective data-driven storytelling that inform decision-making processes. This course is tailored to the challenges pertaining to systems thinking and sustainability.

After completion of the course, the students should be able to:

  • Describe the sustainability data landscape, find relevant data for a project, and develop and implement a basic data management plan.
  • Assess and enhance the quality of datasets for use in a model.
  • Appraise and enhance the utility of a model based on intended purpose, components, data quality, underlying assumptions, and contextual considerations.
  • Identify stakeholders and understand their analytics needs and tailor communication accordingly.
  • Describe the visualization pipeline and the basic principles of visualization, and create visualizations that meet various analytics needs.
  • Describe common narrative frameworks for scientific communication and create data-driven, system-based communication that informs decision-making.


The content of the course reflects the key steps of creating data-driven scientific communication on a sustainability issue for which stakeholders require analytics.

  • Data, information, and knowledge: distinguishing between these interrelated concepts. 
  • Sustainability data: sustainability data sources and types, basics of data management 
  • Data and models: assessing and enhancing data quality, appraising and improving the utility of environmental models such as LCA systems, MFA systems, and the like.
  • Scientific communication to stakeholders: the importance of context and narrative, narrative frameworks, stakeholders’ information and knowledge needs, basics on cognitive and perceptive abilities and their impact on information comprehension.
  • Data visualization: the visualization pipeline, principles of visualization, visualization tools, and examples of successful visualizations.


The course is blended, meaning that it consists of both online and in-class activities. 

Online activities include self-learning activities (short videos, reading materials, formative quizzes) that students should complete before coming to class. In-class activities are used to apply and deepen knowledge of the course content through discussions, workshops, and guest lectures.

Compulsory attendance applies to the majority of in-class activities (as described in the syllabus), to consultation sessions with teachers for group projects, and to the final presentation of group projects (organized as a micro-conference).

Examination including compulsory elements

The course assessment comprises a group project assignment and an individual assignment. To pass the course, the completion of compulsory activities as described in the syllabus is also required.

The group assignment includes intermediate hand-ins, hand-in of the final result of the project, and presentation of the project.

The individual assignment includes intermediate hand-ins in form of short reflections which form the base for the final essay.

Maud Lanau
Maud Lanau
Assistant Professor

My research interests include socioeconomic metabolism and understanding the potential of built environment stocks for circular economy.