Course - Python for Sustainability Analysis - TEP4221
TEP4221 - Python for Sustainability Analysis
About
Examination arrangement
Examination arrangement: Group project
Grade: Passed / Not Passed
Evaluation | Weighting | Duration | Grade deviation | Examination aids |
---|---|---|---|---|
Group project | 100/100 |
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.
- Core Python programming
- Python development environment (VScode, Anaconda, Linter, extensions)
- Scripts, functions, and objects in Python
- Code documentation and management
- Python packages for data science and gridded data
- Project and data management
- Data processing and pipelines for sustainability analytics
- Statistical analysis of key sustainability indicators
- Visualisations of ecological and emissions data
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
- Be able to do all data processing in Python for a research project.
- Be able to write well documented, efficient, and reusable code.
- Be able to give feedback
- Experience in handling real-world data as part of a semester project.
- Acquire a template for your project that you can reuse in the future.
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.
Learning methods and activities
- Lectures
- Pair programming
- Online programming tasks and self-study
- Discussions in plenary or groups
- Pair project work
- Presentations
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 students will get free access to the materials during the course.
The other course material will be distributed via Blackboard.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Extraordinary deadline for course registration: 2023-06-01
Language of instruction: English
Location: Trondheim
Department with academic responsibility
Department of Energy and Process Engineering
Examination
Examination arrangement: Group project
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Autumn
ORD
Group project
100/100
Release
2023-12-01Submission
2023-12-08
08:00
INSPERA
15:00 -
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"