ENVS 193DS

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

Teaching team

Instructor

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

Teaching assistants

photo coming soon!

Name: Amanda Salinas
Email: amandasalinas [at] ucsb [dot] edu
Drop-in hours: TBD
Drop-in location: TBD
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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![](images/151b3f30-b75a-4f57-b0e5-228c4cc3fd02.png){fig-alt="Two monsters decorate the R logo. The blue monster drapes flowers on the grey circle, and the orange monster mains hearts and smiley faces on the blue R." fig-align="center" width="70%" .lightbox}

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

:::

# 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

:::: {.columns}

::: {.column width="50%"}
![](images/headshot-square.JPG){fig-align="center" width="80%"}
:::

::: {.column width="50%"}
**Name:** An Bui  
**Email:** an_bui [at] ucsb [dot] edu  
**Drop-in hours:** Thursdays 3-5 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/) 
:::

::::

## Teaching assistants

:::: {.columns}

::: {.column width="50%"}
photo coming soon!
:::

::: {.column width="50%"}
**Name:** Amanda Salinas  
**Email:** amandasalinas [at] ucsb [dot] edu  
**Drop-in hours:** TBD  
**Drop-in location:** TBD  
**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](https://eds-240-data-viz.github.io/).