Intro to earth data science textbook
intro-to-earth-data-science-textbook Home

Introduction to Earth Data ScienceEarth Lab CU Boulder

Leah Wasser, Jenny Palomino

Welcome to the Introduction to Earth Data Science Textbook!

Key Materials

About the Introduction to Earth Data Science Textbook

Introduction to Earth Data Science is an online textbook for anyone new to open reproducible science and the Python programming language. There are no prerequisites for this material, and no prior programming knowledge is assumed.

This textbook is designed for the Earth Analytics Bootcamp for the Earth Data Analytics Professional Certificate taught by instructors at CU Boulder.

Overview

In this textbook, you will learn how to analyze and visualize earth and environmental science data using the Python programming language. You will also get familiar with a suite of open source tools that are often used in open reproducible science workflows including bash, git and Github.com, and Jupyter Notebook.

This textbook is highly technical, and each chapter covers some aspect of scientific programming and open reproducible science workflows. Additional sections and chapters will continue to be added.

Section 1. Introduction to Open Reproducible Science Workflows
Chapter 1 : Open Reproducible Science Workflows
Chapter 2 : Use Bash to Manipulate Files
Chapter 3 : Jupyter For Python
Section 2. File Formats for Earth Data Science
Chapter 4 : Text File Formats
Chapter 5 : Spatial Data Formats
Section 3. Git and GitHub
Chapter 7 : Git/GitHub For Version Control
Chapter 8 : GitHub for Collaboration
Section 4. Python Code Fundamentals
Chapter 10 : Get Started with Python Variables and Lists
Chapter 11 : Use Python Packages
Chapter 12 : Files, Directories & Paths
Section 6. Scientific Data Structures in Python
Chapter 14 : Numpy Arrays
Chapter 15 : Pandas Dataframes
Section 7. Write Efficient, Clean Code Using Open Source Python
Chapter 16 : Write Clean Expressive Code
Chapter 17 : Conditional Statements in Python
Chapter 18 : Loops in Python
Chapter 19 : Intro to Functions in Python

You May Also Enjoy

Plot Data With Matplotlib

1 minute read

This Week

Calculate Seasonal Summary Values from Climate Data Variables Stored in NetCDF 4 Format: Work With MACA v2 Climate Data in Python

26 minute read

Learn how to calculate seasonal summary values for MACA 2 climate data using xarray and region mask in open source Python.

Calculate Summary Values Using Spatial Areas of Interest (AOIs) including Shapefiles for Climate Data Variables Stored in NetCDF 4 Format: Work With MACA v2 Climate Data in Python

32 minute read

Climate datasets stored in netcdf 4 format often cover the entire globe or an entire country. Learn how to subset climate data spatially and by time slices u...

How to Open and Process NetCDF 4 Data Format in Open Source Python

26 minute read

Historic and projected climate data are most often stored in netcdf 4 format. Learn how to open and process MACA version 2 climate data for the Continental U...