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Hello I am a mathematician currently studying Data Science and I would like to ask for a book that can give me a variety of statistical tools for having more solid conclusions in my analysis of a problem.

For example, it would be useful to know ways to observe correlation between continuous random variables apart from Pearson's coefficient, or between a continuous random variable and a discrete one, etc. I'm also curious about different hypothesis tests that can be used to see if there are significant differences between the results that two algorithms give.

I'm looking for a practical approach that presents a wide variety of methods which I can later study more closely in another source if I'm interested in.

José Alberto
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  • we have a number of similar questions, which will end up recommending the same books: https://stats.stackexchange.com/search?q=%5Bmachine-learning%5D+%5Breferences%5D+books – Sycorax Oct 15 '19 at 23:47

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Coming from CS I found "Applying contemporary statistical techniques" by Wilcox to be useful. For connections to ML, "Elements of statistical learning" by Friedman, Tibisharani and Hastie. Hope this is helpful.

piccolbo
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