ESIIL is committed to building a diverse 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 that will introduce EDS educators and leaders to fundamental EDS education tools. ESIIL Data Short Course participants will gain hands on experience with:
- Understanding and unlocking the potential of EDS to empower their communities.
- Contributing to sustainable and collaborative EDS projects.
- Telling data stories.
Course 1 is open! 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, 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)reate 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 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.
The Details
Instructors: Nate Quarderer & Elsa Culler
Class Start: Monday April 8, 2024
Class End: Friday May 3, 2024
Meeting times:
- Monday 2-4:30pm MST (zoom); synchronous meeting where new material will be presented; sessions will be recorded for asynchronous participation
- Wednesday 2-4pm MST (zoom); synchronous office hours to help answer questions and troubleshoot; sessions will be recorded for asynchronous participation
- 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
- Final presentations: Friday, May 3, 2024 from 12-2pm MST (zoom); synchronous meeting where participants will present their final projects; session will be recorded; those unable to attend will be expected to record their work, post to YouTube and share link through the discussion in the course GitHub repository; provide feedback on classmates’ presentations
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 Monday April 15, 2024 at 4:00pm MST
- NOAA/NCEI Climate data workflow - Due Monday April 22, 2024 at 4:00pm MST
- Culturally relevant map - Due Monday April 29, 2024 at 4:00pm MST
- Final presentation - Due Friday May 3, 2024 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 (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
Be a part of our first cohort of ESIIL Data Short Course participants. Click HERE for the Course Syllabus.
What’s next?
This data short course is first in a series of 4 courses. This is the first course in a 4 course sequence. 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 spring 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 Nathan.Quarderer@colorado.edu or Elsa.Culler@colorado.edu
Past Short Course Students
Congratulations to Dharani Suresh on completing the ESIIL Data Short Course 1! Dharani is a talented researcher in digital agriculture with over nine years of experience integrating deep learning, geospatial analysis, and computer vision to drive sustainable farming solutions. Currently pursuing a PhD at the University of Wisconsin-Madison, Dharani's innovative projects include using advanced remote sensing and modeling to optimize cranberry cultivation. Her dedication to blending technology and sustainability is inspiring. Check out Dharani’s work here on GitHub: https://dharanisureshbabu.github.io/
Congratulations to Adeoye Malumi for successfully completing the ESIIL Data Short Course! Adeoye is a skilled data analyst and Earth scientist passionate about using advanced techniques like machine learning, geospatial analysis, and reproducible science to address environmental challenges. With a strong academic foundation from the University of Benin in geology and hands-on experience in Earth data projects, Adeoye exemplifies the integration of data science with sustainability. We’re excited to see where his journey leads next.