ESIIL is committed to building a community of Earth and Environmental Data Science (EDS) educators. Our Data Short Course is designed to introduce EDS educators and leaders to fundamental EDS education tools.
Participants will learn fundamental teaching tools for open EDS education and research including GitHub, Markdown, and Jupyter Notebooks, and ChatGPT. This beginner-level course will be taught using the Python programming language including the following libraries: OpenStreetMap, pandas, GeoPandas, rioxarray, OpenStreetMap, and matplotlib.
Cloud Computing, Collaboration, and Communication: Demystifying the Fundamental Technology Used by the Environmental Data Science Community
Unit 1 - Create webpage for your class or lab (git, GitHub Pages, Markdown, and Jekyll themes)
Unit 2 - Get your students started with Open & Reproducible Science with Python (GitHub Classroom, GitHub Codespaces, Python, pandas, APIs, andNOAA/NCEI climate time-series data)
Unit 3 - Creating culturally and/or personally relevant EDS curriculum (Python, geopandas, rioxarray, APIs, finding and citing environmental data online), create a culturally relevant map
Unit 4 - Applications (create a module that applies lessons learned; presentations, giving feedback, GitHub code review)
Who is this for?
Modeled after Earth Lab’s Earth Data Analytics Foundations Professional Certificate program, and the NSF-funded Earth Data Science Corps and ESIIL Stars internships, this course is geared toward educators and early careerists interested in incorporating EDS teaching into their existing programs and curricula (biology, ecology, geography, etc.). Participants will learn fundamental teaching tools for open EDS education and research including:
- collaborative web publishing with GitHub
- structuring text Markdown, and
- interactive computing with notebooks.
This free, beginner-level course will be taught using the Python programming language including the following libraries:
- tabular data with pandas
- geospatial vector data with geopandas
- geospatial raster data with rioxarray
- data visualization with OpenStreetMap, matplotlib, and geoviews.
All trainings will be available as both 1) live online workshops and 2) materials for self-paced learning. Building on participation in the short course, we will establish an ongoing open community forum, help desk, and office hours to support continued learning and capacity-building.
The Details
Instructors: Nate Quarderer & Lilly Jones
Class Start: Monday July 13, 2026
Class End: Friday July 24, 2026
Cost: $0
Meeting times:
- Monday & Wednesday 2-4:30pm MST (zoom); synchronous meeting where new material will be presented; sessions will be recorded for asynchronous participation
- Tuesday & Thursday 12-2pm MST (zoom) and by appointment; synchronous office hours to help answer questions and troubleshoot; sessions will be recorded for asynchronous participation
- Throughout the week (GitHub Discussions); asynchronous help from instructors and classmates
Course expectations:
- Participants will be expected to attend synchronous sessions or watch recordings
- Participants will be expected to engage in asynchronous discussion using GitHub
- Participants will be expected to complete the following assignments
GitHub profile/portfolio page - Due Friday July 17, 2026 at 4:00pm MST
NOAA/NCEI Climate data workflow - Due Friday July 17, 2026 at 4:00pm MST
Culturally relevant map - Due Friday July 24, 2026 at 4:00pm MST
Final project - Due Friday July 24, 2026 at 11:59pm MST
- Participants will be expected to spend 5-8 hrs/week working on course modules, watching recorded class meetings, contributing to course discussions, and completing assignments.
Equipment Needed:
Participants will need a computer or tablet with at least 4GB RAM and 50GB disk space. Internet access is necessary to access GitHub, Zoom, and the recordings.
Course Learning Outcomes:
- At the end of the ESIIL Data Short Course, participants will be able to:
- Use GitHub for collaboration
- Create a profile using GitHub pages and Markdown
- Find open, cloud based data for EDS applications
- Perform cloud-based computing using GitHub codespaces
- Complete fundamental EDS tasks with Python
- Open, clean, visualize tabular data using Pandas
- Plot data using Matplotlib
- Open and visualize shapefiles using Geopandas
- Open and visualize gridded (raster) data using Rioxarray
- Create spatial data visualization using OpenStreetMap
- Apply fundamental EDS tools to their teaching and/or research
- Upon completion of the ESIIL Data Short Course, participants will earn the Introduction to EDS certificate of completion from ESIIL
Questions? Please email Nathan.Quarderer@colorado.edu or Lilly.Jones@colorado.edu