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Earth Data Science

Use Data for Earth and Environmental Science in Open Source Python, based off the following: https://www.earthdatascience.org/courses/use-data-open-source-python/

These files contain everything to 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.

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
  • Chapter 13: NETCDF

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