Examination arrangement

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.

• Python packages for data science: NumPy, Pandas, GeoPandas, Matplotlib
• Python development environment (VScode, Anaconda, Linter, extensions)
• Scripts, functions, and objects in Python
• Code documentation and management
• Project and data management
• Data processing and pipelines for sustainability analytics
• Visualisations of ecological and emissions data

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

EP8221 is not open for other than PhD students. The number of students is limited and priority is given to PhD students at IndEcol.

Learning outcome

Knowledge

• 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.

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 explain the difference between an environmental indicator and an environmental impact.
• Can explain the difference between production-based (territorial) and consumption-based environmental accounting.
• Can clearly communicate the results of a Python Project.

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 pitching)

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

Recommended previous knowledge

Experience with handling and analysing structured data.

Students with no programming experience prior to the course may want to get a head start by doing the first DataCamp exercise: Introduction to Python at https://www.datacamp.com/

Course materials

DataCamp courses (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
  • Python Data Science Toolbox

The other course material will be distributed via Blackboard.