Hello again, Jake!
We don’t have our own book. However, here are some ideas:
I really prefer the more math-heavy books. Especially after completing this course, they will help you reinforce what you have learned and give you a better understanding of the machanics of behind the formulas and the techniques used. In my opinion, introductory statistics books will overlap a lot with what we do in this course and would not prove that beneficial.
Light and fun:
An interesting read that may greatly aid you with your understanding is: ‘A Field Guide to Lies and Statistics: A Neuroscientist on How to Make Sense of a Complex World’ by Daniel Levitin. I found the book quite fun, while not technically heavy at all. It looks into different ways people misinterpet statistics (often on purpose).
If you are comfortable with Math:
I’d suggest the: ‘Probability and Statistical Inference’ by Nitis Mukhopadhyay. That must be one of the better readings on the topic (probability, distributions, confidence intervals, hypothesis testing, bayesian statistics).
Then, for regressions (in a business / economics context), I would strongly suggest ‘Econometric Analysis’ by William Greene. That book is especially good, as it has several appendices, which include the linear algebra, probability, etc. that you may not know, or may have forgotten.
But these two books are really math heavy (probability, linear algebra, etc.).
If you want something more programming oriented:
Then one of the classics is ‘Python Data Science Handbook’ by Jake VanderPlas. Probably you know this one. It is especially good because it looks into both NumPy and pandas and how to manipulate data using them.
Hope this helps!