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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.

Books πŸ“š

Mathematics

General Statistics

Advanced Applied Math

Linear Algebra

Programming Information πŸ’Ύ

  • 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

R-cran

Python 🐍

Bash 🐐

SQL - Under Construction

Markdown πŸ”½

Linux 🐧

Machine Learning 🚜

Information Theory

Supervised 🏷️

Unsupervised 🍎

Semi-supervised 🍏

Reinforcement Learning πŸ’ͺ

Articles πŸ“ƒ

  • ⭐ 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 πŸ‘¨β€πŸŽ“ πŸ‘©β€πŸŽ“

Videos πŸ“Ί

Miscellaneous

  • ⭐ Calibre This program is Excellent for organizing PDF's, Epubs and mobi book and article formats. My favorite book and pdf cataloging software πŸ†“

  • ⚠️ 100+ Free Data Science Books Outdated list of books but worth a few minutes. πŸ†“

  • ⭐ 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.

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A Resource for myself and others to learn Data Science, with many emphasis on FREE material. Setup by Matt C.

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