Data Science Practitioner's Reading List

"the no-bs approach"

Greetings,
This list includes material that I have used and would recommend. Whenever possible, I prefer to list FREE material.
Matthew Curcio

⭐ - indicates my personal favorites

First Steps

Coming Soon - Article on How/Where to start.

Great Intro Materials.

⭐ Free Code Camp FREE
Learn Git!
- I like to use GitHub as my software storage bank.
- Git and what you put on your GitHub account IS your new business card.
- GitHub.com is by far the most common storage version control system. FREE
- Want to try Github's tutorial.
- You may also try Gitlab and Bitbucket (Both have FREE options).
⭐ R Programming for Data Science - A GREAT Starter book for easing one into R & Data Science. FREE Book Online 📗
⭐ Exploratory Data Analysis with R - Roger Peng has some great material too!

Books 📚

Mathematics

General Statistics

⭐ Using R for Data Management, Statistical Analysis, and Graphics by N. Horton, et al
Introduction to Statistical Thought By Michael Lavine FREE

Advanced Applied Math

⭐ Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, Tibshirani, Friedman, 2017 FREE
⭐ Introduction to Statistical Learning with Applications in R FREE
Applied Generalized Linear Models and Multilevel Models in R, FREE Book Online 📗

Linear Algebra

Programming Information 💾

Free Code Camp FREE
GoalKicker.com FREE - Programming Books
- BASH, Python, MySQL, GIT, Linux, ...
- Love these; they are more like terse notes for a quick reference.
Syncfusion Ebooks, The Succinctly Series, FREE Resource
- The Succinctly Series has great topics and different languages!

Data Visualization

Slack Group and Website: Data Visualization Society
Mastering Shiny - is an online book on using R to produce an interactive graphic or dashboard.

SQL - Under Construction

PostgreSQL Notes for Professionals :Free: Goalkicker has many books. Although I call them reference type materiale.

Markdown 🔽

⭐ R Markdown: The Definitive Guide by Yihui Xie, et al, FREE Book Online 📗
⭐ R Markdown Cookbook Needed if you are going to use .RMD notebooks and docs. FREE Book Online 📗
For a full list of available Github markdown emoji and codes, check out emoji-cheat-sheet.

Linux 🐧

⭐ Just Enough Linux, Malcolm Maclean FREE

Machine Learning 🚜

⭐ Applied Predictive Modeling by Max Kuhn, K Johnson
Hands-On Machine Learning with R by Bradley Boehmke, et al FREE
Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael Irizarry FREE
⭐ A Course in Machine Learning by Hal Daumé III This is a great illustrative book for beginners. FREE
⭐ Machine Learning, Tom M. Mitchell, ISBN: 0070428077 Very Good
SciKit Learn Tutorial
Building Machine Learning Systems with Python By Richert Coelho & Willi Richert FREE
⭐ Exploratory Data Analysis with R by Roger Peng, FREE Book Online 📗
Syncfusion Ebooks GREAT resource! 🆓
- Keras by James McCaffrey FREE
SVM Tutorial
Pattern Recognition and Machine Learning, Christopher Bishop, 2006, ISBN-13: 978-0387-31073-2 FREE
Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig, ISBN-13: 978-0-13-604259-4
Foundations of Machine Learning - Ed.2018 By Mehryar Mohri, et al FREE
Pattern Recognition & Machine Learning By Christopher Bishop : Heavy on the math FREE
Natural Language Processing with Python Analyzing Text with the Natural Language Toolkit. By Steven Bird, Ewan Klein, and Edward Loper, Very Hands-on guide book FREE

Information Theory

Information Theory, Inference, and Learning Algorithms - By David MacKay I'm interested in reading the section on Hash codes, p.193 FREE

Supervised 🏷️

What are decision trees? by Carl Kingsford and Steven L Salzberg

Unsupervised 🍎

Unsupervised Machine Learning, Michael Foley Book Online 📗

Semi-supervised 🍏

Reinforcement Learning 💪

Articles 📃

⭐ Top 10 algorithms in data mining by Xindong Wu et al I found this 2007 paper really interesting as it was my starting point. The written explanations of the ML tools are not written for beginners in mind, however I feel that it provides a look into which tools are commonly used (as of 2007) and still important overall. I might suggest pulling out the Algos and investigating them in conjunction with other literature.

People 👨‍🎓 👩‍🎓

Andrew Ng - Dr Ng is one of the creators of Coursera, but he also has additional machine learning content
Geoffrey Hinton
- ⭐Learning representations by back-propagating errors
Jeff Leek
- Introduction to Cloud-Based Data Science by Jeffrey Leek
Roger Peng

Videos 📺

FreeCodeCamp FCC has a ton of video lectures on Youtube and which are available thru their own site. The community is welcoming, too.
⭐ Victor Lavrenko High quality videos from his lectures. One of my Favorites.
⭐ Artifical Intelligences, MIT 6.034, w/ Patrick Winston One of my Favorites.
⭐ 3Blue1Brown is produced by Grant Sanderson and has GREAT animations.
⭐ Dr. Bharatendra Rai from Umass, Dartmouth. His videos are very professsional and filled with highly relevant code. Dr Rai's videos are very clear and methodical.
⭐ StatQuest with Josh Starmer Dr Starmer now teaches at NC state. Josh has recently been broadcasting LIVE to boot. BAM!
The MathematicalMonk

Miscellaneous

⭐ Calibre This program is Excellent for organizing PDF's, Epubs and mobi book and article formats. My favorite book and pdf cataloging software 🆓
⚠️ 100+ Free Data Science Books Outdated list of books but worth a few minutes. 🆓
⭐ LeanPub is a great resource for computer related books. Many books are pay what you want. They have books by Roger Peng and Jeff Leek who have written books on D.S.

Online Courses:

⭐ Python for Everybody, 'Py4E' I enjoy Dr. Chuck's youtube lectures. This is a 5 course series provided by Dr Charles Severance, FREE Course follows his FREE Book Online 📗

mccurcio/mcc-ds-material