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The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
Matt Brems

Matt Brems
Global Lead Data Science Instructor, General Assembly

Website | @matthewbrems

Matt currently leads instruction for General Assembly’s Data Science Immersive in Washington, DC, where he helps bridge the gap between theoretical statistics and real-world insights. 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. A recovering politico, Matt was a data scientist for a political consulting firm through the 2016 election. He holds a master’s degree in statistics from the Ohio State University. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, or cuddling with his pug.

Sessions

3:30pm–5:00pm Wednesday, August 22, 2018
Advanced data science, part 2: Five ways to handle missing data in Jupyter notebooks
Reproducible research and open science, Training and education, Usage and application
 Location: Murray Hill B Level: Beginner
Matt Brems (General Assembly)
Average rating: *****
(5.00, 2 ratings)
Missing data plagues nearly every data science problem. Often, people just drop or ignore missing data. However, this usually ends up with bad results. Matt Brems explains how bad dropping or ignoring missing data can be and teaches you how to handle missing data the right way by leveraging Jupyter notebooks to properly reweight or impute your data. Read more.