Business Commons^™

Dos And Don'ts Of Data Science, Ryan Burton

SDSU Data Science Symposium

In an ideal world, we avoid all mistakes in our work. Some mistakes are preventable and others are unavoidable. A few common mistakes in data science that can be minimized include assuming correlation implies causation, modeling with an unrepresentative sample, and focusing on the mean without understanding the distribution. This talk will give an overview of some of the simple yet common mistakes in data science and guidance on how to avoid them.