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