Master’s of Environmental Data Science Program, UC Santa Barbara

*Irrigated fields in the Sahara Desert, southern Egypt. Photo from USGS on Unsplash.*

Allison Horst (ahorst@ucsb.edu)

Mae Rennick (maerennick@ucsb.edu)

Quantitative skills and understanding are critical when working with, understanding, analyzing and gleaning insights from environmental data. In the intensive EDS 212 course, students will refresh fundamental skills in basic math (algebra, uni- and multivariate functions, units and unit conversions), derivative and integral calculus, differential equations, linear algebra, and reading, writing and evaluating logical operations.

The goal of EDS 212 (Essential Math in Environmental Data Science) is to prepare incoming MEDS students with quantitative methods, skills, notation and language commonly used in environmental data science and required for their data science courses and projects in the program. By the end of the course, students should be able to:

**Perform the following by hand and in R:**convert units, basic algebra and working with logs and exponentials; write, interpret and evaluate univariate and multivariate functions; basic derivatives and integrals with univariate and multivariate functions; solve simple differential equations; basic operations with scalars, vectors and matrices; writing and evaluating logicals**Explain and share examples**for how all topics in EDS 212 are useful and used in applied environmental data science**Interpret**examples of applied math & models from environmental science case studies**Work with peers**to solve group tasks, then**communicate**the process of problem solving to the rest of the class

**Course dates:** Monday (2023-08-07) - Friday (2023-08-11)

EDS 212 is an intensive 1-week long 2-unit course. Students should plan to attend all scheduled sessions. All course requirements will be completed between 10am and 4:30pm PT (M - F), i.e. you are not expected to do additional work for EDS 212 outside of those hours, unless you are working with the Teaching Assistant in student hours.

Tentative daily schedule (subject to change):

Time (PST) |
Activity |
---|---|

10:00am - 11:00am | Lecture 1 (60 min) |

11:15am - 12:30pm | Interactive Session 1 (75 min) |

12:30pm - 1:30pm | Lunch |

1:30pm - 2:15pm | Lecture 2 (45 min) |

2:15pm - 3:00pm | Interactive Session 2 (45min) |

3:00pm - 4:30pm | Daily tasks |

Minimum MEDS device requirements (bring to all sessions + charger!)

R

RStudio

Quarto

git

GitHub account

This website was created with gratitude using distill from RStudio by JJ Allaire, Rich Iannone, Alison Presmanes Hill, and Yihui Xie.

This website is one piece of the EDS 212 course materials in addition to lectures, computational activities, discussions, and individual and group tasks, and important materials may exist partially or not at all on this site. While this website is public, it is not meant as a standalone online course.

Other packages used to create this website:

`rmarkdown`

for…pretty much everything: JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone (2021). rmarkdown: Dynamic Documents for R. R package version 2.7. URL https://rmarkdown.rstudio.com.`kableExtra`

for formatted tables: Hao Zhu (2020). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.1. https://CRAN.R-project.org/package=kableExtraSlides were made in R with

`xaringan`

: Yihui Xie (2021). xaringan: Presentation Ninja. R package version 0.20.2. https://github.com/yihui/xaringan