On this page are resources that I find helpful. They're mostly here so that I can get to them easily from anywhere but I hope that other people will find them helpful too.
I compiled a print-friendly, interactable version of DataCamp's Data Science Project Checklist (see below) to make it easier to use in practice.
Data science checklist developed by Dr. RJ Nowling, Milwaukee School of Engineering.
Machine learning checklist developed by Dr. RJ Nowling, Milwaukee School of Engineering.
Chart to choosing the right model produced by scikit-learn.
A flow chart for choosing the most appropriate statistical test(s) made by Antoine Soetewey of statsandr.com.
A glossary of data science terms from the book "Data Science for Business" by Provost and Fawcett .
A business proposal review guide from the book "Data Science for Business" by Provost and Fawcett .
An extensive checklist produced by DataCamp that can, and should, be applied to any data science project.
Personal collection of data science plots that I have found useful.
data-to-viz.com guide to choosing a visualization.
How to Avoid Data Leakage When Performing Data Preparation from machinelearningmastery.com.
ReSTful service checklist developed by Dr. RJ Nowling, Milwaukee School of Engineering.
A reference guide to writing maintainable code and an infrastructure to match.