Course syllabus

ACE590 Sustainability analytics and visualization

Lp1 HT24 (7.5 hp).

Course offered by the department of Architecture and Civil Engineering

Contact details

• Maud Lanau - Main Teacher and Course Responsible. maud.lanau@chalmers.se
• Xin Bian - Course Assistant. xinb@chalmers.se
• Leonardo Rosado - Examiner. rosado@chalmers.se

Course purpose

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.

Schedule

TimeEdit

Course literature

Literature will be made available on the course webpage.

Course design

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).

Learning objectives and syllabus

Learning objectives:

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.

Syllabus

The syllabus is available at this link: Study plan

Examination form

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

• The group assignment includes intermediate hand-ins, hand-in of the result of the project, and presentation of the project. The group assignment contributes 50% to the final grade.
• The individual assignment includes intermediate hand-ins in the form of short reflections at the end of each course module (M1-M5). These form the base for the final individual assignment. The individual assignment contributes 50% to the final grade.
• Compulsory activities take the form of short quizzes on Canvas, that should be completed after getting familiar with the course content on Canvas. Both course content and quizzes will be made available to students in due time.