EP8221 - Python for sustainability analysis


New from the academic year 2023/2024

Examination arrangement

Examination arrangement: Assignment
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Assignment 100/100

Course content

The course provides an introduction to use of data processing, computation and visualisation in the analysis of environmental and socioeconomic data relevant to core sustainability issues.

  • Core Python programming
  • Python development environment (VScode, Anaconda, Linter, extensions)
  • Scripts, functions, and objects in Python
  • Code documentation and management
  • Python packages for data science and gridded data
  • Project and data management
  • Data processing and pipelines for sustainability analytics
  • Statistical analysis of key sustainability indicators
  • Visualisations of ecological and emissions data

Learning outcome


  • Understand programming terminology and be capable of using it.
  • Understand how Python can be used in sustainability analytics.
  • Understand the benefits and drawbacks of different data and code management strategies.
  • Understand the benefits of making an automated data pipeline for your projects.


  • Be able to do all data processing in Python for a research project.
  • Be able to write well documented, efficient, and reusable code.
  • Be able to give feedback
  • Experience in handling real-world data as part of a semester project.
  • Acquire a template for your project that you can reuse in the future.

General competence

  • Understand the challenges of working with sustainability datasets.
  • Become comfortable using programming as a tool to handle data, conduct computations, and visualize results.

Learning methods and activities

  • Lectures
  • Pair programming
  • Online programming tasks and self-study
  • Discussions in plenary or groups
  • Pair project work
  • Presentations

Compulsory assignments

  • Mandatory exercises

Further on evaluation

The grading is based on a Python project. In the end of the course, the students will present their projects in the class. In addition, there are obligatory individual programming exercises on a weekly to biweekly basis.

Specific conditions

Admission to a programme of study is required:
Engineering (PHIV)

Course materials

The course uses the following learning materials of DataCamp (

    • Introduction to Python
    • Intermediate Python
    • Data manipulation with Pandas
    • Introduction to data visualization with Matplotlib
    • Working with geospatial data in Python
    • Python Data Science Toolbox

The students will get free access to the materials during the course.

The other course material will be distributed via Blackboard.

More on the course



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


Term no.: 1
Teaching semester:  AUTUMN 2023

Language of instruction: English

Location: Trondheim

Subject area(s)
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Energy and Process Engineering


Examination arrangement: Assignment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Assignment 100/100





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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