course-details-portlet

EP8221 - Python for sustainability analysis

About

New from the academic year 2023/2024

Examination arrangement

Examination arrangement: Assignment
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Assignment 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

  • Mandatory exercises

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
    • Python Data Science Toolbox

The students will get free access to the materials during the course.

The other course material will be distributed via Blackboard.

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2023

Language of instruction: English

Location: Trondheim

Subject area(s)
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Energy and Process Engineering

Timetable

List view
Calendar view

Examination

Examination arrangement: Assignment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Assignment 100/100

Release
2023-12-01

Submission
2023-12-08


08:00


15:00

INSPERA
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.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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