Ecosystem Science and Sustainability 523c
Environmental Data Science Applications: Water Resources
Welcome!
Welcome to Ecosystem Science and Sustainability 523c: Environmental Data Science Applications: Water Resources! This class is meant to build on the technical skills you learned in ESS 523a, with a focus on water resource examples. We will cover a range of topics, including data science tools, working with vector and raster data, and machine learning.
Structure
In general …
- Mondays will be a lecture,with a mix of slides and discussion.
- Wednesdays will be a lab with a introductory ~30 min technical demo, followed by a hands-on lab due the following week.
- Group work is encouraged, but all assignments should be submitted individually.
Grades
6 labs will be worth 150 points each.
They will be assigned on Wednesdays and due the following Wednesdays before class.
A final project will be optional and worth 150 extra credit points. It will build on your personal website built in ESS 523a.
The total points possible is 1050, with the percentage being taken out of 900 using the traditional 90/80/70/60 scales
Schedule
Component 1: Data Science Tools
Week 01: Level Setting
Tech Talk 01: Quarto/Flextable
Lab 01: Lab 1: COVID Trends
Component 2: Working with Vector Data
Week 02: Projections & Measures
Tech Talk 02: Interactive Mapping
Lab 02: Lab 2: Border summaries
Lab 02: Lab 2: Hints & Tricks
Week 03: Predicates, Simplification & Tesselations
Tech Talk 03: Functions
Lab 03: Lab 3: Dams in the US
Component 3: Working with Raster Data
Week 04: Raster’s in R
Tech Talk 04: STAC
Lab 04: Lab 4: Remote Sensing for Flooding
Component 3: Machine Learning & Time series
Week 05: Feature Engineering & Model Workflows
Tech Talk 05: Live Demo
Lab 05: Lab 5: CAMELS Data Part 1
Week 06: Models, Evaluation, Tuning
Tech Talk 06: Quarto Websites
Lab 06: Personal Website
Week 07: Time Series
Tech Talk 07: Wrap up
Lab 07: Poudre River Forecast
Acknowledgments
This website, including all slides, are made with Quarto. Please submit an issue on the GitHub repo for this course if you find something that could be fixed or improved.
We borrow significant content from the amazing R community and do our best to curate and design course content for students.
Reuse and licensing
Unless otherwise noted (i.e. not an original creation and reused from another source), these educational materials are licensed under Apache 2.