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

**Answer**

- 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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