Resources

I have included some of my commonly used resources below. Enjoy and happy learning!

Books (Print and Digital)

Statistics and Probability

• Probability!, Statistics! and Inference! by Matt DiSorbo
• Bayes Rules! An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu

Metallurgy

• Wills’ Mineral Processing Technology by Barry Wills and James Finch
• Statistics for Mineral Engineers by T.J. Napier-Munn

Python

• Think Python (2nd Edition) by Allen B. Downey
• Learn Python 3 the Hard Way by Zed A. Shaw
• Python for Data Analysis by Wes McKinney
• Python Data Science Handbook by Jake VanderPlas

R

• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• The Elements of Statistical Learning: Data Mining, Inference and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• Statistical Inference via Data Science: A ModernDive into R and the Tidyverse by Chester Ismay and Albert Y. Kim

Websites

• Learn Code the Hard Way
• W3 Schools
• UBC’s Introduction to Machine Learning
• from Data to Viz
• Regular Expressions 101

Blogs & Podcasts

• 911 Metallurgist Blog
• “Applied Machine Learning” by Varada Kolhatkar for the University of British Columbia
• “Machine Learning Guide” by OCDevel