Exploratory data analysis is one of the most essential parts of any data processing pipeline. However, when the magnitude of data is high, these visualizations become vague. This is because if we were to plot millions of data points, it would become impossible to discern individual data points from each other. The visualized output in such a case is pleasing to the eyes but offers no statistical benefit to the analyst. Researchers have devised several methods to tame massive datasets for better analysis. In this short article, we shall look at how the H2O library can aggregate massive datasets that can then be visualized with ease.[Read More]
Breaking the Jargons 