EDS 212: Day 5, End!

Course recap

August 9th, 2024

Day 1 - Algebra warm-up & meeting our tools

Math skills & concepts covered:

  • Basic unit conversions & dimensional analysis
  • Back-of-the-envelope calculations & gut checks
  • Exponents & logarithms (including logistic growth examples)
  • Functions, function notation & terms, evaluating functions, creating a basic function in R
  • Calculating average slope

Tools & workflows stuff:

  • worked in Quarto docs within R Projects, then used usethis::use_git() and usethis::use_github() to connect to a remote repo

Day 1 - Algebra warm-up & meeting our tools

Other stuff:

  • A graph with {ggplot2}
  • Quarto introduction
  • Made sequences with seq() , then evaluated a function over all values of a sequence
  • Installed the {tidyverse}

Day 2: Derivatives!

Math skills / concepts covered:

  • Derivatives (definition of the derivative, what do derivatives mean, examples of applications)
  • A few derivative rules
  • Partial derivatives & what they mean
  • Found & evaluated derivatives (incl. higher order & partials) in R
  • Plotted a function in {ggplot2}

Tools & workflows stuff:

  • More Quarto
  • Continued with git & GitHub
  • Building mental model of git (git mapping)

Day 3: Differential equations

Math skills / concepts:

  • What is integration and what is it useful for?
  • Notation and language for differential equations (e.g. “this is a second order partial differential - equation”)
  • Examples of finding numeric approximations for differential equations
  • Lotka-Volterra equations as a DE example
  • Intro to linear algebra basics (what are scalars, vectors, & matrices), addition, subtraction & dot - products the vectors?

Day 3: Differential equations

Tools & Workflows stuff:

  • New git/GitHub workflow:
    • Fork somebody else’s repo > clone > create an R Project
  • Git commands (in RStudio Terminal)
    • git add
    • git commit
    • git push

Day 4: Matrices, summary statistics & data exploration

Math skills / concepts:

  • Basic matrix algebra (addition, subtraction, multiplication)
  • Representing systems of linear equations w/ matrices
  • Leslie matrices for population projections and projected population structures several iterations into the future
  • Summary statistics (central tendency)
  • Some exploratory visualizations and how to think about them (e.g. boxplots, histograms, pair plots)

Tools & Workflows stuff:

  • GitHub practice (forking, cloning, making a new repo & cloning, etc.)
    • New workflow: create a new repo from scratch on GitHub, > clone > create an R Project.

Day 4: Matrices, summary statistics & data exploration

Other stuff:

  • Making matrices in R
  • Data exploration & summaries (intro) in R (e.g. head(), tail(), dim(), names(), summary/describe, etc.)
  • Pairs plots & histograms with {GGally} , {ggplot2}

Day 5: Summary statistics, basic probability theory

Math skills / concepts:

  • Data spread (variance, standard deviation)
  • Confidence interval introduction
  • Basic probability theory (union, intersection, conditional probability)
  • Intro to Boolean logic & operations

Tools & Workflows stuff:

  • Git in terminal / git bash
  • git pull
  • git collaboration (2 collaborators, both pushing to main)


EDS 221 - Scientific Programming Essentials


Data representation, types and structures; programming and function development; iteration, conditionals, functions and objects; documentation; testing and troubleshooting; tidy data structure; and a dive to data wrangling and visualization.

Now: Please fill out your course evaluation