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Resources

Here is a list of great resources for your enjoyment (not necessarily Python!). The resources are less technical - focusing on the technology from a scientist's point of view, rather than that of a computer scientist.

The list is roughly sorted from most widely applicable at the top, to least widely applicable at the bottom.

  • Earth and Environmental Data Science course book, by Ryan Abernathey
  • Python for Data Analysis, 3E by Wes McKinney
    • A practical guide for scientists, teaching essential Python skills, pandas, and NumPy for efficient data manipulation, analysis, and visualization. Wes McKinney is also the creator of Pandas, the "go to" package for data analysis in Python.
  • Software Carpentry
    • Software Carpentry is a lesson program - geared towards researchers - equipping individuals with essential data and computational skills. Courses include:
      • The Unix Shell
      • Version control with Git
      • Programming with Python
      • Plotting and programming with Python
      • Programming with R
      • R for Reproducible Scientific Analysis
  • Scientific Python Library Development Guide
    • This guide goes over developing Scientific Python Packages, complete with a repo-review tool to give suggestions for your project to adhere to Python best practices, and a cookiecutter template to streamline the setup of new Python packages. This guide is maintained by the scientific Python community (i.e., maintainers from various popular scientific Python packages) for the benefit of fellow scientists and research software engineers.
  • PyOpenSci Learning Resources
    • A collection of learning resources from the PyOpenSci community, an international community of scientists and software engineers promoting open science.
  • Xarray tutorial

Extras

Other material

Open Science Communities