Methods for Dealing with Missing Data in Python and R


Like many other aspects of data science, there is a fair amount of art and skill involved with how to deal with missing data. I’ll explore some introductory techniques and packages in Python and R to help you move beyond simply dropping missing observations or imputing the mean or median from columns with missing data.

About the Speaker

Sean Reed
Sean is a Senior Data Scientist at Galvanize where he mentors and trains students to become data analysts and data scientists.