Course - Python for Sustainability Analysis - TEP4221
Python for Sustainability Analysis
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
Limited admission to classes. For more information: https://i.ntnu.no/wiki/-/wiki/English/Admission+to+courses+with+restricted+admission
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 Blackboard.