Bruno Gonçalves offers an overview of the fundamental concepts and ideas behind human visual perception and explains how it informs scientific data visualization. To illustrate these concepts, Bruno shares practical examples using matplotlib and seaborn.
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.