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I'm looking for books similar to Introduction to Statistical Learning with Applications in R (ISLR), which is not too rigorous in terms of the mathematical treatment, but still able to provide you the intuition about the methods? I'm particularly looking at this topics:

  • Generalized Linear Models
  • Time Series Analysis
  • Survival Analysis
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    $\begingroup$ Is “using R” a constraint? $\endgroup$ – Dimitriy V. Masterov Jul 9 '20 at 10:08
  • $\begingroup$ Not really. It don't need to contain code at all, but i doubt any "easy" books won't. But to answer your question, I'm programming language agnostic $\endgroup$ – Benj Cabalona Jr. Jul 9 '20 at 10:17
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  • For time series analysis: "Forecasting Principles and Practices" by Hyndman and Athanasopoulos is absolutely excellent and is roughly on the same order of mathematical complexity as ISLR (i.e. enough, but not too much). It has the additional bonus of being available for free online, and having many code examples. It has one weak point: It doesn't do a good job of providing business context or intuitive aspects of TS modeling. For that I recommend "Demand Forecasting for Managers" by Stephan Kolassa and Enno Siemsen.
  • For GLM's: Chapter 4 of "Machine Learning and Pattern Recognition" by Bishop gives a brief, but pretty good explanation of GLMs within the context of classification, and does so at the level of theoretical math you are looking for. No code samples though, and I don't think a free version was ever released.
  • For Survival Analysis, I can't give you one specific reference. But in general, I would recommend looking in Operations Research or Industrial Engineering textbooks and course materials for the mid-level theoretical content and intuitive explanations that you are seeking.
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  • $\begingroup$ Hyndman's book is an excellent book. However I find it's more like a tutorial of how to use R software for Time Series. It lacks quite a bit of theoretical details. $\endgroup$ – StatsStudent Jul 10 '20 at 7:51
  • $\begingroup$ Pattern Recognition and Machine Learning is available for free here: microsoft.com/en-us/research/people/cmbishop/prml-book $\endgroup$ – vigos Jul 15 '20 at 8:29
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If you’re interested in Bayesian Inference then there’s a wonderful book (goes into GLMs quite a lot) called Statistical Rethinking by Richard McElreath. The second edition is just out and there’s lecture series on YouTube. The most recent series (called Winter 2019 IIRC) follows the second edition.

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  • $\begingroup$ I wasn't a fan of this book. The examples provided were often too wordy for my liking and the corresponding statistical concepts seem to get lost. $\endgroup$ – StatsStudent Jul 10 '20 at 7:54
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Haven't read this new edition, but the first edition is a classic, so this one, available starting September 2020, will be a great reference for sure.

I second the recommendation of "Statistical Rethinking" by Mooks, that's a great one.

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    $\begingroup$ Thanks! I think I'll wait for the 2nd edition to be available on Google books. $\endgroup$ – Benj Cabalona Jr. Jul 9 '20 at 6:41
  • $\begingroup$ Definitely wait for the second edition! $\endgroup$ – Gijs Jul 9 '20 at 7:12
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For GLMs I recommend Faraway's Extending the Linear Model with R. I would also recommend Frank Harrell's Regression Modeling Strategies, which provides a nice in depth explanation of regression as a whole and various extensions including survival modeling. Both textbooks include code in R.

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For survival analysis, Kleinbaum (2013) - Survival Analysis -- A self-learning text is straightforward with R examples. It's even freely available on Springer now due to COVID-related university lockdowns: https://link.springer.com/book/10.1007%2F978-1-4419-6646-9. I think Frank Harrell's Regression Modelling Strategies is also freely available now for the same reason. At any rate, it was a short while ago.

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From some of the same authors, there is another book focused more on the intuition and practicalities than the the math:

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer Verlag. https://web.stanford.edu/~hastie/ElemStatLearn/

Efron and Hastie also have a great book that is doable even if you skip over the math:

Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press.

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