See Tutorials for the Google Colab notebooks used during the tutorials and Exercises for the notebooks used during the coding sessions. If you are taking this course for credits, don't forget to push the completed notebooks in the Exercises directory of your cloned repository.
These tutorials and exercises adapt excellent online resources to efficiently introduce the basics of Python, in particular:
- An Introduction to Earth and Environmental Data Science by Ryan Abernathey, Kerry Key, and Tim Crone (License).
- The Urban Computing Skills Lab bootcamp by Mohit Sharma, Federica Bianco, and Stanislav Sobolevsky (License).
- Cognitive Class.ai: "Introduction to Python" by Vin Busquet and Raph Trajano (License).
- CS 345: Machine Learning Foundations and Practice
- Python Data Science Handbook
- mlcourse.ai
- DataCamp data-science and machine learning courses
- Geopandas official website: Introduction to GeoPandas
- Automating GIS process
- Use Data for Earth and Environmental Science in Open Source Python
- The Shapely User Manual
- Geospatial Analysis with Python and R
- Introduction to Geospatial Data in Python
There are many excellent tutorials to get started with Python, such as:
- Programming with Python by © Software Carpentry and © Data Carpentry (License), if you need a tutorial that focuses on the fundamentals and goes at a slower pace than this tutorial.
- The Python Basics page from Machine Learning for Climate and Energy by Bruno Deremble and Alexis Tantet (License). This page is appropriate if you are looking for a quick tutorial covering the libraries to get started with machine learning for the environmental sciences.
- Pythia Foundations, which is a community learning resource for Python-based computing in the geosciences
If you are struggling with some of the exercises, do not hesitate to:
- Use a direct Internet search, or stackoverflow
- Ask your neighbor(s), the teacher, or the TA for help
- Debug your program, e.g., by following this tutorial
- Use assertions, e.g., by following this tutorial