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SDG232 Data analytics for sustainable future

Course description for academic year 2022/2023

Contents and structure

Data analytics provide different methodologies that are useful to understand, describe and model raw data in order to draw conclusions and identify patterns. This course will provide students with introductory tools and methods necessary to process and analyze data in the context of sustainable development. By taking this course, students will understand the opportunities and challenges of Data Analytics and Sustainability. Topics will include visualization techniques, data wrangling, linear models, time series, forecasting, and statistical machine learning.

The course will be a combination of lectures, seminars, and weekly exercises for which we will be using an open-source program such as R and RStudio. At a practical level, students will have the opportunity to explore an assortment of data analytics techniques and apply them to problems involving real-world data. During the seminars, the data analysis will consist of: initial exploratory data analysis, selection of statistical analysis or modeling approach, implementation, and discussion of results. Students will work on computer exercises throughout the semester while reviewing the major data analytical techniques to a range of sustainability/climate change-related topics.

The course is a part of the Norwegian West Coast SDG Educational Initiative at The Faculty of Business Administration and Social Sciences: Sustainable Economics, Management and Innovations (SEMI).

Learning Outcome

Knowledge based learning outcome

  • Key concepts, tools and approaches for data analytics on different data sets.
  • Techniques for modeling and extracting features from large data sets.
  • Theoretical concepts and the motivations behind different data analytics approaches.
  • Knowledge relevant for SDG goal 3, SDG 4, SDG 9, SDG 11, and SDG 12.

Skill based learning outcome

  • Be able to analyze data.
  • Ability to apply acquired knowledge for understanding data and select suitable methods for data analysis.
  • Translate development problems into specific data objectives.
  • Be able to apply data analysis methodologies in practice to a range of sustainability-related topics with R programming language.

General competences

  • Discuss data analytics within the sustainable development framework.
  • Solve real-world data-driven problems, data wrangling, and information extraction tasks.
  • Understand existing methods and tools used to leverage data analytics.
  • Trade data analytics tasks for sustainability such as data exploration and visualization, regression, and prediction.

Entry requirements


Teaching methods

Lectures, seminars and exercises.

Compulsory learning activities

Two written assignments.


Students will build individual portfolios with samples of problem solving, written explanations of how to solve problems, charts, graphs, and computer analyses conducted. A pass/fail grading system will be applied to each portfolio. Students who fail will be given additional assignments.

Examination support material

All written and printed materials allowed.

More about examination support material