Abstract: So you have missing data... how do you fix it? If you're asking this question, you're well aware of the fact that missing data plagues nearly every data set. Whether missing values are represented by blanks, NAs, or -999, they're everywhere and need to be handled. The most common approaches are to ignore or drop the missing data, but these result in a lot of negative consequences. A slightly more sophisticated approach is to fill the missing data in with some value, but also this has a lot of negative and unintended side effects. We'll cover why these methods are subpar and how to properly handle missing values. The goal is for you to walk away with a solid, intuitive understanding of how to handle missing data and some of the practical tips for how you can implement these techniques in a real setting.
Bio: Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.