Resources

Data

Google Dataset Search lets you search for publically available data on any topic.
Google Trends lets you see how trends in what people search for on Google vary over time for any given topic.
OpenStreetMap is an amazing resource for all sorts of data... roads, trails, rivers, lakes, buildings, etc.!
PAD-US provides spatial data for all parks and protected areas in the United States.

Daniel Lakens' free Coursera course, Improving your Statistical Inferences, is a great tool to improve your understanding of stats. Also check out his blog.
If you liked the previous course, check out his other course, Improving your Statistical Questions.
G*Power is a tool you can download to help determine the sample size you'd need for your study.

People often ask how I learned programming. Most of it is through trail and error and lots of Google Searches. There are a TON of wonderful and smart people on Stack Overflow who are willing to help answer your questions.
Garrett Grolemund & Hadley Wickham's book "R for Data Science" is very helpful and can be found for free here.
Interested in making visualizations in R? Claus O. Wilke's book "Fundamentals of Data Visualization" can be found for free here.
Want to master ggplot for visualizations in R? The "R Graphics Cookbook" by Winston Chang is online for free here.
Dr. Emily Burchfield has put together R-Markdown tutorial documents on how to make maps and do spatial analysis in R. Check them out here!
Already know R? Info from Hadley Wickham's book "Advanced R" can be found for free here.
Interested in machine learning?!? Free book by Bradley Boehmke & Brandon Greenwell called "Hands-On Machine Learning with R" is available here. (I have not read this yet, but have heard good things! I'm putting it here so I don't forget to read it.)
Check out the whole array of free online R books here. (Hooray open science!)

The tidyverse (especially dplyr): This makes all data cleaning a million times easier.
sp and sf: Great packages for working with spatial data
tmap: Useful for easily visualizing spatial data.
elevatr: Useful for obtaining elevation data from anywhere in the world.
daymetr: Useful for easily obtaining weather estimates on a 1 km scale, anywhere in the US (using Daymet data).
osmdata: Interested in working with road data? Trail data? Building data? Water data? This package has got you covered! Downloading OpenStreetMap data directly from this package is much easier than downloading it off their website.

4th National Climate Assessment talks about risks, impacts, and adaptation in the U.S.
IPCC AR5 is the most recent global climate change synthesis report. AR6 will be released in 2021-2022.
NA-CORDEX has data on downscaled climate change projections out to 2100 under RCP scenarios.
The World Meteorological Organization also has data on global climate change projections out to 2100 under RCP scenarios using CMIP5 sceanrio runs. These projections are on a 2.5 degree scale.