Python for R Users

Plan

I will continue to use screenshots as a tutorial device and I will draw upon my experience in data science consulting to highlight practical data parsing problems. This is because choosing the right tool and technique and even package is not so time consuming but the sheer variety of data and business problems can suck up the data scientist’s time that can later affect quality of his judgment and solution.

Intended Audience

This is a book for budding data scientists and existing data scientists married to other languages like SPSS or R or Julia. I am trying to be practical about solving problems in data. Thus there will be very little theory.

Afterthoughts

I am focused on practical solutions. I will therefore proceed on the assumption that the user wants to do data science or analytics at the lowest cost and greatest accuracy, robustness, and ease possible. A true scientist always keeps his mind open to data and options regardless of who made whom. The author finds that information asymmetry and brand clutter have managed to confuse audiences of the true benefits of R versus Python versus other languages. The instructions and tutorials within this book have no warranty and you are doing so at your own risk.

As a special note on formatting of this manuscript, the author mostly writes on Google Docs, but here he is writing using the GUI LyX for the typesetting software LaTex, and he confesses he is not very good at it. We do hope the book is read by business users, technical ...