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As a researcher and instructor, I'm looking for open-source books (or similar materials) that provide a relatively thorough overview of data science from an applied perspective. To be clear, I'm especially interested in a thorough overview that provides material suitable for a college-level course, not particular pieces or papers.

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closed as off-topic by demongolem, senshin, Bill the Lizard, Sean Owen, Konstantin V. Salikhov May 14 '14 at 8:40

  • This question does not appear to be about data science, within the scope defined in the help center.
If this question can be reworded to fit the rules in the help center, please edit the question.

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    $\begingroup$ List questions are usually not suited for Stack Exchange websites since there isn't an "objective" answer or a way to measure the usefulness of an answer. Having said that, one of my recommendations would be MacKay's "Information Theory, Inference, and Learning Algorithms." $\endgroup$ – Ansari May 14 '14 at 0:38
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    $\begingroup$ This question appears to be off-topic because it is asks for a favorite resource. On other SE sites, this would immediately be closed. Since this is a new site, we still have to decide if this is a valid question here $\endgroup$ – demongolem May 14 '14 at 1:16
  • $\begingroup$ Fair enough regarding what constitutes a "valid" question, although on other SE sites this question would not be immediately closed as you've stated: e.g., 2495 votes, 1440 votes, 168 votes, and so on. There's great interest for these kinds of questions, even if this isn't deemed the right place. $\endgroup$ – statsRus May 14 '14 at 2:35
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    $\begingroup$ @statsRus: Try posting a question like that to SO, and it'll be closed; these questions exists because they have historical significance, but they are not considered good, on-topic questions for Stack Exchange sites, so please do not use them as evidence that you can ask similar questions here. $\endgroup$ – blunders May 15 '14 at 21:08
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One book that's freely available is "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman (published by Springer): see Tibshirani's website.

Another fantastic source, although it isn't a book, is Andrew Ng's Machine Learning course on Coursera. This has a much more applied-focus than the above book, and Prof. Ng does a great job of explaining the thinking behind several different machine learning algorithms/situations.

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Data Science specialization from Johns Hopkins University at Coursera would be a great start. https://www.coursera.org/specialization/jhudatascience/1

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There is free ebook "Introduction to Data Science" based on language

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