course-details-portlet

EP8221

Python for sustainability analysis

Choose study year
Credits 7.5
Level Doctoral degree level
Course start Autumn 2025
Duration 1 semester
Language of instruction English
Location Trondheim
Examination arrangement Group project

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
  • Individual project work
  • Presentation (scientific presentation or sustainable innovation pitch)

Compulsory assignments

  • Obligatory assignments

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 (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

Course coordinator

Lecturers

Department with academic responsibility

Department of Energy and Process Engineering