Greetings,
These Data Science resources are among my favorites. I have used and would recommend them.
Whenever possible, I prefer to list FREE material.
Matt C.
🌟 - Represents Favorites ; 📗 - Represents Free Online Books
- 🌟 Using R for Data Management, Statistical Analysis, and Graphics, by N. Horton, et al
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🌟 Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, Tibshirani, Friedman, 2017 FREE 📗
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🌟 Introduction to Statistical Learning with Applications in R, FREE 📗
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Applied Generalized Linear Models and Multilevel Models in R, FREE 📗
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Linear Algebra As an Introduction to Abstract Mathematics, FREE
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🎥 Linear Algebra with Dr Strang - This is a playlist of his MIT classes. It's ok if you skip the first class, it's course info.
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Free Code Camp, FREE
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GoalKicker.com FREE - Programming Books 📗
BASH, Python, MySQL, GIT, Linux, ... These are more like terse notes for a quick reference. -
Syncfusion Ebooks, The Succinctly series has some small guides but pretty good & FREE 📗
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Slack Group and Website: Data Visualization Society
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Mastering Shiny - is an online book on using R to produce an interactive graphic or dashboard. FREE Book Online 📗
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🌟 The caret Package by Max Kuhn, FREE Online 📗
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Advanced R, by H. Wickham, FREE Online 📗
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🌟 blogdown: Creating Websites with R Markdown If you use R alot and want an easy way to demo your work this is great, FREE Online 📗
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🌟 bookdown: Authoring Books and Technical Documents with R Markdown, FREE Online 📗
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R Graphics Cookbook, 2nd edition, FREE Online 📗
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A ModernDive into R and the Tidyverse, FREE Online 📗
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Text Mining with R, FREE Online* 📗
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Swirl is an excellent beginner tool for learning R: (https://swirlstats.com/)
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🌟 I enjoy Dr. Chuck's youtube lectures Coursera Python for Everybody 5 course series provided by Dr Charles Severance, FREE Online 📗
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🌟 Python Data Science Handbook, by Jake VanderPlas
There are so many alternatives:
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https://jupyter4edu.github.io/jupyter-edu-book FREE Online 📗
- 🌟 Data Science at the Command Line, by Jeroen Janssens, FREE Online 📗
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PostgreSQL Notes for Professionals FREE, Goalkicker has many books. Although I would call them reference-type material.
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🌟 R Markdown: The Definitive Guide, by Yihui Xie, et al, FREE Online 📗
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🌟 R Markdown Cookbook Needed if you are going to use .RMD notebooks and docs. FREE Online 📗
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For a full list of available Github markdown emoji and codes, check out emoji-cheat-sheet.
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🌟 Applied Predictive Modeling by Max Kuhn, K Johnson, FREE Online 📗
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Hands-On Machine Learning with R by Bradley Boehmke, et al, FREE
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael Irizarry, FREE
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🌟 A Course in Machine Learning by Hal Daumé III This is a great illustrative book for beginners. FREE
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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable: (https://christophm.github.io/interpretable-ml-book/) FREE
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🌟 Machine Learning, Tom M. Mitchell, ISBN: 0070428077, Very Good
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Building Machine Learning Systems with Python, by Richert Coelho & Willi Richert, FREE
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🌟 Exploratory Data Analysis with R by Roger Peng, FREE Online 📗
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Syncfusion Ebooks, GREAT resource!
Keras, by James McCaffrey, FREE 📗 -
Pattern Recognition and Machine Learning, by Christopher Bishop, 2006, ISBN-13: 978-0387-31073-2, FREE
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Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig, ISBN-13: 978-0-13-604259-4
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Foundations of Machine Learning - Ed.2018, by Mehryar Mohri, et al, FREE
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Pattern Recognition & Machine Learning, by Christopher Bishop : Heavy on the math, FREE
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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, Inference, and Learning Algorithms, by David MacKay
Interested in reading the section on Hash codes? p.193, FREE 📗
- What are decision trees?, by Carl Kingsford and Steven L Salzberg
- Unsupervised Machine Learning, by Michael Foley, FREE Online 📗
- 🌟 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.
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Andrew Ng
Dr Ng is one of the creators of Coursera, but he also has a great machine learning course(
See: https://www.deeplearning.ai -
Geoffrey Hinton
🌟Learning representations by back-propagating errors -
Jeff Leek
Introduction to Cloud-Based Data Science, by Jeffrey Leek
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🌟 FreeCodeCamp FCC has a ton of video lectures on Youtube and which are available thru their own site. The community is welcoming, too.
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🌟 Victor Lavrenko High quality videos from his lectures. One of my Favorites.
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🌟 Artifical Intelligences, MIT 6.034, w/ Patrick Winston, One of my Favorites.
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🌟 3Blue1Brown is produced by Grant Sanderson and has GREAT animations.
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🌟 Dr. Bharatendra Rai at 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
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🌟 Calibre
This program is Excellent for organizing PDF's, Epubs and mobi book and article formats. My favorite book and pdf cataloging software FREE -
🌟 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.