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Can people provide the best textbook they have used for learning data science? I know this is somewhat difficult because you have no basis of comparison but if you can just give your opinion on a book and why, I'd greatly appreciate it.

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    $\begingroup$ I don't think there's any such thing: first, data science is very broad and not so well defined. A more well defined domain is Machine Learning, but not everything in data science is ML. Second, data science (as well as ML) is recent and things change very fast: any general-purpose book is bound to become obsolete within a few years, so it's unlikely that any book can ever become a reference. $\endgroup$ – Erwan Sep 6 '20 at 18:05
  • $\begingroup$ How could this question be answered with facts and citations? I'm simply asking what are some respected textbooks on data science. I'm sure it's a question that many people would like to know. smh.. $\endgroup$ – QuietInMontana Sep 18 '20 at 3:44
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As already pointed out by the first comment, data science is a very broad field and not sharply defined.

But I think since you like to get started with data science and you're looking for resources, I share some of my recommendations with you (in no particular order).

  • Very applied, less academical, with lots of code examples: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
  • The Deep learning bible, more academical, great overview: Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
  • A classic for Machine learning, covers a lot of topics, very educational with great explanations: Pattern Recognition and Machine Learning by Christopher M. Bishop
  • If you're more into statistics: An Introduction to Statistical Learning: with Applications in R by James, Witten, Hastie & Tibshirani
  • Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
  • The classic RL reference: Reinforcement Learning by Richard S. Sutton & Andrew G. Barto

This is only a partial list focusing more on theory and classic data science algorithms. Keep in mind there are many many more books, e.g. I didn't mentioned any resources for Big Data, Data Analytics, Data Mining etc. whatever one would throw into the data science bucket. Further it's possible that this list might be outdated in a few years.

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