Course content

The course gives an introduction to data processing, data analysis, and visualisation in the context of sustainability analysis.

• 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).

The sustainability theme of the course may vary from year to year. Recently, it has been focused on circular economy, sustainable innovations, and system's perspective to sustainability.

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 data related to sustainability
• 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
• Discussions in plenary or groups
• Academic debating
• VSC demos
• Online programming tasks and self-study
• Hackaton
• Group project work
• Presentation (scientific presentation or sustainable innovation pitch)

Compulsory assignments

• Obligatory programming assignment

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

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 Canvas.