These are all resources you may find helpful in your first few years (e.g. to supplement the core courses and/or starting research). Unless noted otherwise, they are all freely available online.

Probability, Statistics, Machine Learning and Optimization

Probability Theory and Examples by Rick Durrett (
– One of the preferred grad level probability textbooks.

Jeff Miller has an excellent series of youtube videos
– Probability primer ( This course covers some topics in probability (634-635) and stat theory (654-655).
– Machine learning ( This course overs many topics in stat theory (654-655) and applied stats (664-665). It also covers topics in machine learning and bayesian stat courses.

Introduction to Statistical Learning ( and Elements of Statistical Learning (
– These are great places to turn for your first (and second) foray applied statistics and machine learning.

Michael Jordan’s suggested reading list for statistics PhD: (not free)

The deep learning book (
– Introductory/intermediate level textbook form some of the masters.
– Also a good book to machine learning and optimization.

Convex Optimization by Vandenberghe and Boyd  (
– The standard introduction to optimization.
– Also see the course webpage ( and Stephen Boyd’s youtube lectures (

Optimization Methods for Large-Scale Machine Learning (
– Overview of many of the modern optimization methods that statisticians/machine learning researchers should at least be aware of.


These are helpful resources for getting started in R/Python and for learning some more advanced topics.

Introductory R

R for Data Science http by Hadley Wickham (
– Fantastic, free, online textbook for introductory to intermediate R.

STOR 320: Intro to Data Science (
– Undergrad course at UNC which introduces R and data science.

Introductory Python

Python Data Science Handbook by Jake Vanderplas (
– Introduction to doing statistics/machine learning in Python.

Computational Statistics in Python by Cliburn Chan  (
– Covers a huge number of topics in computational statistics from advanced python to MCMC to GPU computing.

Other Helpful Resources and More Advanced Topics

Computational Linear Algebra by (
– Covers things like PCA, robust PCA, non-negative matrix factorization, large scale linear regression all in Python.

Lot’s of small coding examples in Python/R:

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