ESIIL is committed to building a community of Earth and Environmental Data Science (EDS) educators. Our Data Short Course, launching this spring, is the first in a series of 4 courses to introduce EDS educators and leaders to fundamental EDS education tools. ESIIL Data Short Course participants will gain hands-on experience with:

1. Understanding and unlocking the potential of EDS to support their communities.
2. Contributing to sustainable and collaborative EDS projects.
3. Telling data stories.

Course 1: Click HERE for the Course Syllabus.

Participants will learn how to incorporate GitHub (GitHub Classroom, GitHub Codespaces, and GitHub Pages) and Python (pandas, GeoPandas, rioxarray, matplotlib, folium) into their curriculum or existing research model. At the end of the data short course, participants will apply these lessons to a current class or research project.

Data Short Course 1 

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, and NOAA/NCEI climate time-series data) 
• Unit 3 - Creating relevant EDS curriculum (Python, geopandas, rioxarray, APIs, finding and citing environmental data online)
• 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, 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 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 folium, 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.

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 (Course 1), 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 Folium
  • 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 microcredential badge from CU Boulder

What’s next? 

This data short course is first in a series of 4 courses. Each spring, ESIIL will offer another iteration of this course, with each successive course building on its predecessor and growing in complexity. These courses are designed for participants to come in and out to meet their own developmental needs. Courses will be recorded and offered again with opportunities for micro credentialing for anyone who may have missed the first offering. Stay tuned for more information about the next cycle of the ESIIL Data Short Course offered summer of 2025.

• Data Short Course 1 - Cloud Computing, Collaboration, and Communication: Demystifying the Fundamental Technology Used by the Environmental Data Science Community

• Data Short Course 2 - Data structures in Python 

• Data Short Course 3 - Spatial data in Python

• Data Short Course 4 - Clean & Reproducible Code

Each course will provide participants with the opportunity to develop their own lessons that apply skills learned during the training. 

Questions? Please email esiil@colorado.edu.

Stay tuned for announcements about the next ESIIL Data Short Course in Summer 2025!