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---
title: "Intro Data Science Course Summary"
author: "Matthew Ross"
date: "5/4/2022"
output:
html_document:
toc: true
toc_float: true
---
# Purpose
This page is a standing resource to refer back to
for any additional guidance as you work on your
own data science projects after our classes (Intro Dat Sci, Data Sci - Water).
I have tried to arrange
the resources somewhat in "chronological" order, meaning
in order of increasing complexity in your R journey.
If you think things are missing or you want to see more,
please add an issue and I will add what resources I can.
# Intro Material
These classes assumed some amount of R knowledge before you
started, an assumption that turned out to be mostly wrong. However, there
are plenty of Intro R materials out there! These include:
## Truly Basic R Intro
- [Stat 158](https://csu-r.github.io/Module1/) - Vectors, data frames, installing R, etc...
- [RStudio Materials](https://education.rstudio.com/learn/beginner/) - A series of
videos, books, and more to get you started in R
- [RStudio Primers](https://education.rstudio.com/learn/beginner/) - Interactive
online coding. Really excellent if you can make it through all of the primers.
## Tidyverse and more Intro
- [R for Data Science](https://r4ds.had.co.nz/introduction.html) - Covers all of
the basic intro material, from a tidyverse perspective. As discussed, this is
one way to find solutions in R, it happens to be my preferred way, but there are
lots of Base R ways that work just fine! This is a big book, and should be thought
of as a reference!
- [Stat 159](https://csu-r.github.io/Module2/) - A CSU specific course for an
intro to the tidyverse
## Additional Core Introductory Material
- [Happy Git With R](https://happygitwithr.com/) - Phenomenal resource for learning
git, GitHub, and RStudio integration. As discussed, using Git/GitHub/R together
is a really important way to keep workflows open and robust
- [R Markdown](https://bookdown.org/yihui/rmarkdown/#preface) - The primary
book for learning more about R Markdown and all of its quirks
- [Cheatsheets](https://www.rstudio.com/resources/cheatsheets/) - Short
clear documents that cover so much material from dplyr to shiny apps. Great
for quick references
# Geospatial R
- [Geocomputation with R](https://geocompr.robinlovelace.net/intro.html) - Intro
to all things geospatial analysis, visualisation, etc... in R. Heavy on geometric
operations and basics
- [Spatial Data Science](https://keen-swartz-3146c4.netlify.app/) - More deep
investigation of spatial data science skills with a focus on the details of
geometric and statistical operations. Written by the package creators of core
geospatial packages `sf`, `stars`, `terra` and more!
# Statistics and Machine Learning in R
- [Intro to probability/stats](https://bookdown.org/speegled/foundations-of-statistics/) - Great intro to basic stats concepts and ideas about probability with an R focus
- [Hands on Machine Learning in R](https://bradleyboehmke.github.io/HOML/) - Fantastic
hands on approach to machine learning in R
- [Intro to Bayes](https://statswithr.github.io/book/) - Nothing we discussed in
class but a key approach to understand
# Miscellaneous
- [Web Scraping](https://steviep42.github.io/webscraping/book/) - Clear and exhaustive
introduction to webscraping including `RSelenium`, `rvest`, and other core packages.
- [Time Series](https://otexts.com/fpp3/) - Primarily focused on forecasting, but
generally useful for time series approaches.