I had to read a lot of books in graduate school. Some were life-changing, and others were forgettable.

If I could bring a reading list back in time for my ‘first year’ graduate self, it would include the following:

Bayesian Data Analysis

Third Edition, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin

Probably the most useful book I’ve ever owned.

The Art of R Programming

by Norman Matloff

This book made me less bad at programming in R.

Causality

Second Edition, by Judea Pearl

Ecology is complicated. We often lack replicated controlled experiments with random treatment assignment. This book helps with that.

Statistics for Spatio-Temporal Data

by Noel Cressie and Christopher Wikle

A thoughtful treatment of hierarchical modeling in a spatial, temporal, and spatiotemporal context. Has breadth with a healthy dose of outside references for depth.

Ecological Models and Data in R

by Benjamin M. Bolker

Covers fundamental ideas about likelihood and process-oriented modeling while building R proficiency.

Bayesian Models: A Statistical Primer for Ecologists

by N. Thompson Hobbs & Mevin B. Hooten

An introduction to the process of model building and estimation for non-math/stats oriented readers.

Data Analysis Using Regression and Multilevel/Hierarchical Models

by Andrew Gelman and Jennifer Hill

A gentle introduction to multilevel modeling, with plenty of graphics and integration with R.

Statistical Inference

Second Edition, by George Casella and Roger L. Berger

Essential for understanding the mathematical and probabilistic foundations of statistics. Read it after brushing up on calculus.

Linear Algebra

by George Shilov

I wish I had taken a class in linear algebra as an undergraduate, but I instead had to catch up in my first year of grad school. This book made it relatively painless.

Single and Multivariable Calculus

by David Guichard and friends

Because I took a few calculus classes in high school and college and didn’t know why.

Mathematical Tools for Understanding Infectious Disease Dynamics

by Odo Diekmann, Hans Heesterbeek & Tom Britton

Mathematical epidemiology is a huge topic. This book introduces common models and approaches from first principles, with plenty of problems along the way to make sure you’re following along. Read it with a notebook and pencil handy.