Use Data for Earth and Environmental Science in Open Source PythonEarth Lab CU Boulder


Welcome to the Use Data for Earth and Environmental Science in Open Source Python Textbook!

Key Materials

About the Use Data for Earth and Environmental Science in Open Source Python Textbook

Use Data for Earth and Environmental Science in Open Source Python is an intermediate and multidisciplinary online textbook that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of heterogeneous “big” scientific data.

This textbook assumes that readers have reviewed the Introduction to Earth Data Science textbook or are familiar with the Python programming language, Jupyter Notebook, and git/GitHub.

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

Overview

In this textbook, you will learn computationally intensive techniques to address scientific questions using a suite of different types of publicly available data including:

  • Satellite and airborne lidar and spectral remote sensing data,
  • Data collected using distributed in situ (on the ground) sensor networks
  • Social media data, and
  • Basic demographic data.

This textbook is highly technical, and each chapter covers some aspect of scientific programming with Python and open reproducible science workflows.

Section 1. Time Series Data in Python
Chapter 1 : Time Series Data in Pandas
Chapter 1.5 : Flood Returns Period Analysis in Python
Section 2. Intro to Spatial Vector Data in Python
Chapter 2 : Spatial Data in Python
Chapter 3 : Processing Spatial Vector Data in Python
Section 3. Introduction to Raster Data in Python
Chapter 4 : Intro to Raster Data in Python
Chapter 5 : Processing Raster Data in Python
Section 4. Spatial Data Applications in Python
Chapter 6 : Uncertainty in Remote Sensing Data
Section 5. Multispectral Remote Sensing Data in Python
Chapter 7 : Intro to Multispectral Remote Sensing Data
Chapter 8 : NAIP
Chapter 9 : Landsat Data
Chapter 10 : MODIS Data
Chapter 11 : Calculate Vegetation Indices in Python
Section 6. Introduction to Hierarchical Data Formats in Python
Chapter 12 : HDF4
Section 7. Introduction to API Data Access in Open Source Python
Chapter 15 : APIs
Chapter 16 : Twitter Data
Section 8. Earth Data Science Workflows
Chapter 12 : Design and Automate Data Workflows
Section 9. Data Stories
Chapter 20 : Flood overview
Chapter 21 : Intro to Lidar Data
Chapter 22 : Wildfire Overview

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