Welcome to ENVS 193DS, Spring 2024!

Two fuzzy monsters standing side-by-side outside of a door frame through which is a magical wonderland of different R communities, with a “mind blown” rainbow coming out of the one closest to the door. A welcome mat says “Welcome.”.

Artwork by @allison_horst

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

Instructional team

Instructor: An Bui
Email: an_bui [at] ucsb.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

TA: Caitlin Nordheim-Maestas
Email: caitlinnordheim [at] ucsb.edu
Drop-in hours: Tuesdays 9:30 - 10:30 AM
Drop-in location: Noble Hall 2201
More about me: cnordheim-maestas.github.io

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 Csik’s Data Visualization and Communication course.

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Artwork by [@allison_horst](https://allisonhorst.com/)

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# 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  

# Instructional team

:::: {.columns}

::: {.column width="40%"}

![](images/headshot-square.JPG)

**Instructor:** An Bui  
**Email:** an_bui [at] ucsb.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](https://an-bui.com/)  
:::

::: {.column width="10%"}
<!-- empty column to create gap -->
:::

::: {.column width="40%"}

![](images/caitlin-headshot.jpeg)

**TA:** Caitlin Nordheim-Maestas  
**Email:** caitlinnordheim [at] ucsb.edu  
**Drop-in hours:** Tuesdays 9:30 - 10:30 AM   
**Drop-in location:** Noble Hall 2201   
**More about me:** [cnordheim-maestas.github.io](https://cnordheim-maestas.github.io/)   
:::

::::

### 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 Csik's [Data Visualization and Communication course](https://samanthacsik.github.io/EDS-240-data-viz/).