These are a collection of books & resources I'm either in the process of reading, going to read, or have already read and love referencing.

All code included here is my own interpretation unless otherwise stated, and should in no way reflect on the authors & works used. I am grateful to all these amazing people who took the time to publish such excellent materials; especially those who used open source standards. I have created this collection so they may can provide as much value to the next student as they did to me.

By Allen Sweigart

My journey through Al Sweigart's fun and straightforward take on some Python. His course is available as a book, a video course on Udemy, and for free online. I'd consider this a 'Python 101' book, with the additional bonus of some very practical examples.

By Allen Downey (Repurposed from Roger Labbe)

An interactive take on Allen Downey's excellent primer on Bayesian Statistics, using some Python examples. His work is available as a book, a LaTeX file with supporting code in his Github Repository, and for free online. I have borrowed an amazing interactive Jupyter notebook version created by Roger Labbe to append with my own learning as I work through it.

By Joel Grus

A clone of supplemental materials for Data Science from Scratch by Joel Grus. It is part of the venerable "O'Reilly" family, and is a the closest I've come to a Data Science Handbook.

By Matthew A. Russell

A clone of Mining the Social Web, 2nd Edition. Available as a book, a website, and as a [virtual machine.](https://rawgithub.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/html/_Appendix A - Virtual Machine Experience.html) Easily some of the most practical recipes for dealing with social data, and the basis for a lot of my scripts at my current job.

By Chris Smith

An introductory book on F# that comes highly recommended, and also provides a relatively friendly entry into the mysterious world of functional programming which a friend described to me as 'writing code like you write math'. The repo included here is merely a mirror of the publicly available scripts that follow along side the book, which I am still working through.

By Chris von Csefalvay

An amazing Gitbook on Julia; a young but fascinating technical language built for scientific computing. It exists as a hybrid between OOP and FP principles, which allows it "write like Python but run like C" thanks to an incredible number of under-the-hood optimizations from leaders in the field. It suffers slightly from its novelty, especially with regard to package support and availablity of novice-level documenation (which is why many advocate against its use in production environment), but I fell in love with it as soon as I watched this video of it in action, and I couldn't wait to get started. Big shout out to Chris for his amazing repo designed for "people who need to get a job done, not computer scientists".

By Luciano Ramalho

If you google 'Mastering Python', I guarantee sooner or later you will run into this book. Heralded by many as one of the best end-to-end Python books around, I honestly cannot wait to get started with this book. Julia and R have become my personal favorites, there is just no denying the ease of use, modularity, and sheer popularity of Python (especially in regards to Data Science), and I intend to do my best to improve my capabiliites in it. I have borrowed the actual author's notebook repo to append with my own learning, and I intend to start on this as soon as I can.

By Sam Abrahams et al.

I found out about this book on Reddit! I've always been curious by Tensorflow, especially with its usage of the beautiful TensorBoard capabilities, but I was told its previous iterations weren't quite on the same level as other popular ML packages. However, with a combination of open source principles and the sheer scale and support of Google, it has definitely become a force to be reckoned with. I also love reading the Google Research Blog, and I'm keen to play with the system presumably powering such fascinating insights; I can't wait to see it in action!

By Drew Neil

A series of exercises following Practical Vim. Basically a group of arcane commands that let you take advantage of one of the most powerful (and occasionally tempermental) text editors ever designed. I personally prefer using it via its VimR incarnation, and I highly recommend it for macOS users. To be honest, I still use Atom whenever I can get away with it, but a friend of mine recommended at least a functional knowledge of it, for terminal only / server-side environments.

Maintained by Clare Corthell

A hand made open-source curriculum for Data Science as designed by some of the best in the field. This is probably the single most important repository included in here, and I would highly recommend you start here if you haven't already gotten started with data science; it's where I did (after the Coursera course from Andrew Ng blew my mind).

Maintained by Zipfian

An excellent collection of resources I recieved as part of my acceptance into the awesome Galvanize data science bootcamp.

Maintained By Donne Martin

A clone of the Data Science iPython Notebooks Repository. A continually updated collection of iPython/Jupyter notebooks on every subject from scikit-learn to AWS. Chances are if it's related to data science, there's a beautifully maintained notebook about it somewhere in here.

Maintained By Torch

A clone of the Torch Tutorials to better understand Torch, one of the most trusted ML framework currently out there. It has the benefits of being built on Lua, a procedural scripting language that can be generally understood in 15 minutes. I discovered this repo by checking out it's cheatsheet, and hope to to start converting these into iTorch notebooks pretty soon.

This repository is licensed under GNU General Public License v3.0. This may not apply to all works included herein.