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TEP4221

Python for Sustainability Analysis

Choose study year
Credits 7.5
Level Second degree level
Course start Autumn 2025
Duration 1 semester
Language of instruction English
Location Trondheim
Examination arrangement Group project
Special deadlines for course registration
Autumn: 2025-06-01

About

About the course

Course content

The course gives an introduction to data processing, data analysis, and visualisation in the field of environmental science.

  • Python packages for data science: NumPy, Pandas, GeoPandas, Matplotlib
  • Python development environment (VScode, Anaconda, Linter, extensions)
  • Writing clear scripts that are easy to follow
  • Data and code documentation and management
  • Presentation of results:
    • Scientific presentation
    • Innovation pitch

The course is designed for industrial ecology students and provides the programming skills needed in the following Masters' courses (IO analysis, LCA, MFA).

Learning outcome

Knowledge

  • Understand and can use Python programming terminology
  • Know the benefits and drawbacks of different data and code management strategies
  • Can explain the concepts of circular economy and business models
  • Can explain why systems perspective is important in sustainability analysis
  • Can give various examples of Python applications for sustainability analysis

Skills

  • Can independently create a Python project and write well documented, efficient, and reusable code
  • Can create, modify, delete, and use Python environments
  • Can import, export, and process large datasets with Pandas
  • Can create clear and useful plots with Pandas and Matplotlib
  • Can communicate clearly the results of a Python Project
    • Presenting/pitching skills
    • Written skills

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
  • Acquire a template for a Python project that can be reused in the future

Learning methods and activities

  • Lectures
  • Pair programming
  • Online programming tasks and self-study
  • Discussions in plenary or groups
  • Pair project work
  • Presentation (scientific presentation or sustainable innovation pitch)

Compulsory assignments

  • Obligatory programming assignment

Further on evaluation

The grading is based on a group/pair 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

Course materials

The course uses the following learning materials of DataCamp (https://www.datacamp.com/):

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

The other course material will be distributed via Blackboard.

Subject areas

Contact information