In the past year or two I've had several friends approach me about learning statistics because their employer/organization was moving toward a more data-driven approach to decision making. (This brought me a lot of joy.) I firmly believe you don't actually need a fancy degree and tens of thousands of dollars in tuition debt to be able to engage with data, glean insights, and make inferences from it. And now, thanks to many wonderful statisticians on the Internet, there is now a plethora of freely accessible resources that enable curious minds to learn the art and science of statistics.
First, I recommend installing R and RStudio for actually using it. They're free and what I use for almost all of my statistical analyses. Most of the links in this post involve learning by doing statistics in R.
Okay, now on to learning stats…
There's Data Analysis and Statistical Inference + interactive companion course by Mine Çetinkaya-Rundel (Duke University). She has also written the OpenIntro to Statistics book (available for free as a PDF).
Free, self-paced online courses from trustworthy institutions:
- Carnegie Mellon University's Open Learning Initiative:
- Harvard University's edX: Data Analysis for Life Sciences 1: Statistics and R
- DataCamp's Introduction to R
- University of Toronto's Statistics: Making Sense of Data on Coursera
Not free online courses from trustworthy institutions:
- Johns Hopkins University's Data Science Specialization on Coursera
- DataCamp's A Hands-on Introduction to Statistics with R course
- Introduction to Data Science with R by Garrett Grolemund of RStudio,
Free miscellaneous resources:
- Learn R in R with swirl
- Probability and statistics ebook by UCLA's Statistics Online Computational Resource
- AP Statistics Tutorial
- Noam Ross's Introduction to ggplot (a R package for data visualization)
- Jenny Bryan's list
- Introductory Statistics with R by Peter Dalgaard
- Doing Data Science: Straight Talk from the Frontline by Cathy O'Neil
- Statistics in a Nutshell by Sarah Boslaugh
- Principles of Uncertainty by Jay Kadane (free PDF at http://uncertainty.stat.cmu.edu/)
Phew! Okay, that should be enough. Feel free to suggest more in the comments below.