The Paper Trail

Hi all! I'm a huge bibliophile and quite avid about education. In the context of my work in data science as a machine learning engineer, I've had to learn a lot of material extremely quickly. Every new model build brings with it a new learning curve. Below I've listed by favorite online tutorials, books, and research papers for learning about various interesting topics. You can also find the recommendations below, and more, in this google sheet: Paper Trail Google Sheet

Books

Here are a few of my favorite text books for learning about topics in data science.

Topic	Book Title	Main Author	Reading Level	Source
Causal Inference	Causality	Pearl	Expert	LINK
Causal Inference	Counterfactuals and Causal Inference	Winship	Expert	LINK
Causal Inference	Causal Inference: What If 2	Robins	Practitioner	LINK
Cyber Security	Network Security and Cryptology	Musa	Introductory	LINK
Cyber Security	Counter Hack Reloaded	Skoudis	Introductory	LINK
Evolutionary Algorithms	Essentials of Metaheuristics	Luke	Practitioner	LINK
Filtering	Bayesian Filtering and Smoothing	Sarkka	Practitioner	LINK
Graphs	Networks, Crowds, and Markets	Kleinberg	Expert	LINK
Graphs	Network Science	Barabasi	Introductory	LINK
Graphs	Networks	Newman	Practitioner	LINK
Machine Learning	Advanced Data Analysis from an Elementary Point of View	Shalizi	Expert	LINK
Machine Learning	Generalized Linear Models	Nelder	Introductory	LINK
Machine Learning	Foundations of Data Science	Kannan	Introductory	LINK
Machine Learning	Pattern Recognition and Machine Learning	Bishop	Practitioner	LINK
Machine Learning	Doing Analysis using Regression and Multilevel/Hieratchical Models	Gelman	Practitioner	LINK
Machine Learning	Pattern Classification	Duda	Practitioner	LINK
Markov Chain Monte Carlo	Handbook of Markov Chain Monte Carlo	Gelman	Introductory	LINK
Markov Chain Monte Carlo	Monte Carlo Statistical Methods	Casella	Introductory	LINK
Markov Chain Monte Carlo	Markov Chain Monte Carlo in Practice	Gilks	Practitioner	LINK
Markov Chain Monte Carlo	Monte Carlo Strategies in Scientific Computing	Liu	Practitioner	LINK
Natural Language Processing	Neural Network Methods for Natural Language Processing	Goldberg	Practitioner	LINK
Neural Network	Neural Networks and Deep Learning	Nielsen	Introductory	LINK
Neural Network	Deep Learning	Goodfellow	Practitioner	LINK
Optimization	Algorithms for Optimizations	Kochenderfer	Introductory	LINK
Probabalistic Graphical Models	An Introduction to Probabilistic Graphical Models	Jordan	Practitioner	LINK
Probabalistic Graphical Models	Machine Learning: A Probabilistic Perspective	Murphy	Practitioner	LINK
Probabalistic Graphical Models	Probabilistic Graphical Models	Koller	Practitioner	LINK
Quality Control	Introduction to Statistical Quality Control	Montgomery	Introductory	LINK
Reinforcement Learning	Deep Reinforcment Learning Hands-On	Lapan	Practitioner	LINK
Reinforcement Learning	Reinforcment Learning An Introduction	Sutton	Practitioner	LINK
Statistics	Statistical Analysis	Kachigan	Introductory	LINK
Statistics	A Modern Introduction to Probability and Statistics	Dekking	Introductory	LINK
Statistics	Statistical Inference	Casella	Introductory	LINK
Statistics	Handbook of Biological Statistics	McDonald	Introductory	LINK
Statistics	Mostly Harmless Economics	Pischke	Introductory	LINK
Statistics	Experimental Design	Cox	Introductory	LINK
Statistics	Sampling Techniques	Cochran	Introductory	LINK
Statistics	Field Experiments	Gerber	Introductory	LINK
Statistics	Monte Carlo Strategies in Scientific Computing	Liu	Practitioner	LINK
Statistics	Design of Observational Studies	Rosenbaum	Practitioner	LINK
Statistics	Sampling: Design and Analysis	Lohr	Practitioner	LINK
Statistics	Graphical Models, Exponential Families, and Variational Inference	Jordan	Practitioner	LINK
Statistics	Handbook of Statistical Methods	NIST	Practitioner	LINK
Statistics (Bayesian)	Gaussian Processes for Machine Learning	Rasmussen	Expert	LINK
Statistics (Bayesian)	Statistical Descision Theory	Berger	Expert	LINK
Statistics (Bayesian)	Statistics for Spatio-Temporal Data	Cressie	Expert	LINK
Statistics (Bayesian)	Geostatistics: Modeling Spatial Uncertainty	Chiles	Expert	LINK
Statistics (Bayesian)	Probabalistic Reasoning in Intelligent Systems	Pearl	Expert	LINK
Statistics (Bayesian)	Probabilistic Programming and Bayesian Methods for Hackers	Davidson-Pilon	Introductory	LINK
Statistics (Bayesian)	Statistical Rethinking	McElreath	Introductory	LINK
Statistics (Bayesian)	A Student's Guide to Bayesian Statistics	Lambert	Introductory	LINK
Statistics (Bayesian)	Think Bayes	Downey	Introductory	LINK
Statistics (Bayesian)	Theory of Probability	Jeffreys	Introductory	LINK
Statistics (Bayesian)	The Foundations of Statistics	Savage	Introductory	LINK
Statistics (Bayesian)	Doing Bayesian Data Analysis 1	Kruschke	Introductory	LINK
Statistics (Bayesian)	Probability Theory	Jaynes	Practitioner	LINK
Statistics (Bayesian)	Bayesian Data Analysis	Gelman	Practitioner	LINK
Statistics (Bayesian)	Bayesian Probability Theory: Applications in the Physical Sciences	Linden	Practitioner	LINK
Statistics (Bayesian)	Information Theory, Inference, and Learning Algorithms	MacKay	Practitioner	LINK
Statistics (Bayesian)	The Bayesian Choice	Roberts	Practitioner	LINK
Statistics (Bayesian)	Bayesian Artificial Intelligence	Korb	Practitioner	LINK
Statistics (Bayesian)	Bayesian Nonparametrics	Ghosh	Practitioner	LINK
Statistics (Bayesian)	Bayesian Nonparametrics	Muller	Practitioner	LINK
Statistics (Bayesian)	Modeling and Reasoning with Bayesian Networks	Darwiche	Practitioner	LINK
Statistics (Bayesian)	Bayesian Learning for Neural Networks	Radford	Practitioner	LINK
Statistics (Bayesian)	Bayesian Forcasting and Dynamic Models	Harrison	Practitioner	LINK
Time Series	Introduction to Time Series	Brockwell	Introductory	LINK
Time Series	Elements of Forecasting	Diebold	Introductory	LINK
Time Series	Time Series Analysis	Hamilton	Introductory	LINK
Time Series	Introduction to Time Series Analysis and Forecasting	Montgomery	Introductory	LINK
Time Series	Nonlinear Time Series	Fan	Practitioner	LINK
Time Series	Timeseries	Hamilton	Practitioner	LINK

1. Python version of examples: Doing Bayesian Data Analysis
2. Python version of examples: Causal Inference: What If

Courses

If text books aren't your thing, here are a few awesome online courses.

Topic	Course Title
Reinforcement Learning	Berkeley Deep Reinforcement Learning
Reinforcement Learning	University College London Reinforcement Learning
Signal Processing	Filtering in Python
Machine Learning	Lazy Programmer Inc.
Statistics	Statistics and Probability
Statistics	MIT Introduction to Probability and Statistics
Linear Algebra	MIT Linear Algebra Sprint 2010
Machine Learning	Stanford CS229
Statistics	Seeing Theory
Statistics	StatLec
Data Visualization	Fundamentals of Data Visualization

Blogs

A big part of my professional development is education, both teaching and learning. I'm a firm believer that we should constantly strive to further our education throughout our lives. I've always enjoyed taking classes and reading books on a diverse range of subjects from glass blowing to the study of the Greek classics. Here I've listed a few blogs I've found to be extremely influencial on my education in datascience. Make sure to check out my own personal blog too!

Radford Neal's Blog
Eric Jang's Blog
While My MCMC Gently Samples
Stratechery
The Morning Paper
Lillian Weng
Darren Wilkinson's Blog
District Data Labs
Distill
Hacker News
Jason Wittenbach's Github
Mack Sweeney's Github
Josh Touyz' Blog
Keegan Hines' Blog
covert.io

Podcasts

I also love listening to podcasts on my commute to work or on long hikes. Here are a few of my favorite technical podcasts.

Security Now
Command Line Heroes (Redhat)
Stratechery
Lex Friedman AI Podcast