ENVS 193DS

Three monsters in witch hats (covered in hex stickers for different R packages) work together to brew up a data science report on the banks of a wild river. They are surrounded by equipment for water quality measurements (like a Secchi disk, field journal, sampling bottles, and YSI). The background is a distant snow-covered mountain and pine trees. In the river alongside the witches are a curious turtle, mayflies, a jumping fish, and a dragonfly. Made with Angie Reed and Alena Reynolds for their 2024 Posit Conference Talk.

Course description

Environmental scientists use data to understand the world around them. In this class, we’ll learn about the tools used in environmental science and beyond to work with, analyze, and communicate about data.

By the end of the quarter, students will be able to:
1. Describe basic concepts of probability and statistics
2. Identify appropriate statistical analyses to test hypotheses
3. Conduct statistical analyses and visualize data using the R programming language
4. Implement best practices for reproducible analysis and collaborative work
5. Interpret and contextualize statistical results in general concepts from environmental studies

Teaching team

Instructor

Name: An Bui
Email: an_bui [at] ucsb [dot] edu
Drop-in hours: Wednesdays 3:30 - 5:30 PM
Drop-in location: At the tables outside the UCen 1st floor (facing the lagoon)
More about me: an-bui.com

Teaching assistants

Name: Thuy-Tien Bui
Email: thuy-tienbui [at] ucsb [dot] edu
Teaching day: Thursdays
Drop-in hours: Thursdays 2:00 - 3:00 PM
Drop-in location: At the tables outside the UCen 1st floor (facing the lagoon)
More about me: https://thuy-tienbui.github.io/

TA: Grace Lewin
Email: glewin [at] ucsb [dot] edu
Teaching day: Fridays
Drop-in hours: Fridays 11:00 AM - 12:00 PM
Drop-in location: Library 6541
More about me: TBD

Acknowledgements

I took much of my inspiration for this course from Allison Horst’s Environmental Data Science and Statistics course, Sam Sambado’s Biometry course, and Sam Shanny-Csik’s Data Visualization and Communication course.

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