Documents my Python journey through the introductory statistics textbook Goos & Meintrup - "Statistics with JMP: Graphs, Descriptive Statistics and Probability" (2nd, 2015) (see Wiley).

This book was recommended to me by a good friend (and better researcher) who started his deep-dive in statistics with this book. I aim to not perform any statistics using JMP, but rather use the amazing Python datascience ecosystem (Pandas, SciPy, SciKit-Learn, NumPy, Seaborne, MatPlotLib etc.) to perform the same (if not better!) analyses as done in the book.

All uploaded datasets were either copy-pasted from the book (typically the smallest sets) or freely obtained from the amazing free resource (Kaggle)[https://www.kaggle.com/datasets/].

I hope this codebase can serve as a good refernce for what I deem to be the most important take-aways of the book.