We use these tools combined with nbgrader which is a part of the Jupyter ecosystem and developed by Jess Hamrick to collect, grade, and return feedback for student assignments which are completed using Jupyter Notebooks.

Accessing Teaching Data Sets and Spatial Data Exploration Using EarthPy

Accessing data in a reproducible way that is both suitable for teaching specific data and programming approaches can be challenging. The code needed to complete some commonly implemented spatial data workflows is complex, and can be simplified using standardized functions and approaches. To address these unique challenges, we developed and maintain a Python package called EarthPy that makes working with spatial data and downloading teaching data subsets easier.

A Classroom in the Cloud: JupyterHub, Google Cloud, and the Earth Analytics Python Environment

One of the biggest challenges associated with teaching data science to scientists is environment setup. Specifically for Python, there are numerous options for setting up environments, some of which can be frustrating to get working. To address this challenge we have developed:

 We Contribute to and Support Free and Open Source Software for Science

We use and are committed to Free and Open Source Software (FOSS) in all of our teaching efforts. Free and open source software directly supports open reproducible science because everyone has the same access to these tools. This also accelerates science by allowing scientists to easily and quickly build upon each other's shared workflows. As such we have contributed back to several tools including GeoPandas and nbgrader. We are also leading pyOpenSci, an effort focused on building a diverse community around Python FOSS tools for science.