PODS Reading List
Not your average course reading list! We know that without formal training, it can be quite daunting entering the world of data science and programming. Below are some resources that can help demystify concepts such as data, AI, and statistics. Some also touch on the consequences of untethered data science development.
Book: Weapons of Math Destruction
by Cathy O'Neil. This book does a superb job at exposing how big data and predictive modelling can exacerbate and promote social inequities.
Book: The Plain Person's Guide to Plain Text Social Science by Kieran Healy. One of our bootcamp preparation texts, this book introduces basic quantitative analysis using R.
Online Courses: Khan Academy
KA is beloved resource for students seeking extra assistance and visual aids. We recommend their unit on Statistics & Correlation.
Podcast Episode: The Crime Machine Parts I & II
by Reply All on Gimlet. This episode does a great job at exploring the consequences of CompStat, a software based in statistics used by police forces around the world. This episode focuses on the NYPD.
Book: Naked Statistics
by Charles Wheelan. Using real-life examples, Wheelan does a great job at filling in the theoretical gaps of traditional statistics training.
Twitter: The #RStats Community
If you're having trouble with your code, you might get some help with the #rstats hashtag. The R community is known for their friendliness and desire to help each other out. For real!
Project: Center for Ethics, Society, and Computing
at the University of Michigan. Explore their website and goals!
Community: #TidyTuesday by R for Data Science. Take part in a weekly event that encourages the use of R with a cool new dataset! The community welcomes beginners and experts. It is coordinated by friend of PODS, @thomas_mock.
