EDS 212: Essential Math in Environmental Data Science

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)

Course description

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.

Learning objectives

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: 

Predictable daily schedule

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

Expected sessions (subject to change)

Day / session Lecture topics Interactive session Activities
1 (morning) Basic algebra review, units and unit conversions, exponents, logarithms By hand practice problems; Meet our R tools, basic operations, our first R function
1 (afternoon) Functions (interpreting & evaluating), reading graphs, slope By hand practice problems; R projects, storing objects, creating vectors and sequences Day 1 Tasks - Handout
2 (morning) Definition of the derivative Derivatives by hand and in R
2 (afternoon) Higher order & partial derivatives Partial & higher order derivatives in R Day 2 Tasks - Handout
3 (morning) Differential equations, and solving them numerically Interpreting differential equations, solving numerically
3 (afternoon) Introduction to linear algebra basics Making & basic algebra with vectors and matrices Day 3 Tasks - Handout
4 (morning) Linear algebra continued Linear algebra continued - vectors, matrices, a Leslie matrix example
4 (afternoon) Essential summary statistics and describing data Data exploration and summary statistics - getting started Day 4 Tasks - Handout
5 (morning) Summary statistics continued, basic probability theory Basic probability problems, summary statistics, R Markdown customization
5 (afternoon) Boolean logic and operators, hypothesis test primer, course recap Relational & logical operators in R

Course requirements


About this website

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: