Statistics makes heavy use of the normal distribution for mathematical reasons, and also because the assumption of normality is often reasonable. Many natural phenomena are normally distributed, in fact.

That said, statisticians make use of dozens of distributions. As Bayesian statistics becomes mainstream, it's more important than ever that statisticians and data scientists have a good understanding of probability.

I've been cracking the books myself, and some books I've found helpful include:

- Introduction to Probability (Bertsekas and Tsitsiklis)
- Introduction to Probability Models (Ross)
- Essentials of Probability Theory for Statisticians (Proschan and Shaw)
- Handbook of Statistical Distributions (Krishnamoorthy)
- Statistical Distributions (Forbes et al.)

Hardin and Hilbe's book in the Generalized Linear Model is an excellent overview of GLMs. In addition to hierarchical modeling and mixture models, there are many extensions such as GAM, VGAM and GAMLSS, Quantile Regression, etc. Frank Harrell's book Regression Modeling Strategies is a gold mine, IMO.

There are also SEM extensions of the GLM, not to mention factor analysis, latent class clustering and a gigantic number of other unsupervised methods.

I can also recommend the Journal of the American Statistical Association to those who believe statistics is outdated and inflexible...:-) Lots of crazy urban legends circulating in some corners of the data science community.

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