IE501988 - Sustainability Analytics


New from the academic year 2023/2024

Examination arrangement

Examination arrangement: Portfolio
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio 100/100

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 interdisciplinar, combining courses on artificial intelligence (e.g., machine learning), data science, sustainability science, and decision science.

Learning outcome


- Be able to explain in detail central concepts, technologies and algorithms proposed for sustainability analytics.

- Be familiar with important applications of sustainability analytics.


Learning outcomes:

After successful completion of this course students are expected to be able to:

  • understand specific challenges and proposed solutions related to the management of large volumes of data in sustainability analytics applications;

-clearly explain problems, algorithms, and their formulas;

-understand well how can be used in ;

-qualitatively and quantitatively compare the characteristics, (dis)advantages, formulas, and performance of a number of key algorithms;

-design and implement effective solutions based on chosen algorithms, to solve practical problems.

Learning methods and activities

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

Further on evaluation

The portfolio consists of a predefined number (3-10) of theoretical and project assignments. The portfolio contains assignments that are carried out, digitally documented and submitted during the term. Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio. The assessment is an overall evaluation of the portfolio.

Specific conditions

Admission to a programme of study is required:
Master in engineering in Simulation and Visualization (880MVS)

Course materials

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

More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English

Location: Ålesund

Subject area(s)
  • ICT and Mathematics
  • Engineering Subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of ICT and Natural Sciences


Examination arrangement: Portfolio

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Portfolio 100/100 INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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