Course syllabus for Sustainability analytics and visualization

Course syllabus adopted 2024-02-14 by Head of Programme (or corresponding).

Overview

  • Swedish nameDataanalys och visualisering för hållbarhet
  • CodeACE590
  • Credits7.5 Credits
  • OwnerMPTSE
  • Education cycleSecond-cycle
  • Main field of studyEnergy and Environmental Systems and Technology
  • DepartmentARCHITECTURE AND CIVIL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 28127
  • Maximum participants30
  • Block schedule
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0124 Project, part A 4 c
Grading: TH		4 c0 c0 c0 c0 c0 c
0224 Written and oral assignments, part B 3.5 c
Grading: TH		3.5 c0 c0 c0 c0 c0 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Courses equivalent to at least 15 ECTS in environmental science and sustainability is a prerequisite. At least 15 ECTS demonstrating computing skills is also required.
Courses in which students have used a fair amount of sustainability-related data (e.g., Environmental Systems Analysis (VMI101), Life Cycle Assessment (VTM081), Urban Metabolism (ACE155)) are a suitable background.

Aim

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.

Learning outcomes (after completion of the course the student 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.

Content

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.

Organisation

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).

Literature

Literature will be made available on the course webpage.

Examination including compulsory elements

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

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.