I am constantly amazed by the energy and momentum around data science. Only a few years ago, I would be met with a blank stare when I told someone I planned on going to grad school for machine learning. Today, there is no need for my “it's like computer science, linear algebra, and statistics had a combined love child” analogy as most people instantly respond with “Oh, like AI!”
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Parallel coordinates is one way to visually compare many variables at once and to see the correlations between them. Each variable is given a vertical axis, and the axes are placed parallel to each other. A line representing a particular sample is drawn between the axes, indicating how the sample compares across the variables.
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Often times, when we're looking at a mass of data, we're trying to get a sense for relationships within that data. Who is the leader in this social group? What is a common thread between different groups of people? Such relationships can be represented hundreds of ways graphically, but few are as powerful as the classic network diagram.
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There are data visualizations. There are web applications. If they had a baby, you'd get a data application.
Data applications are a big part of where our data-driven world is headed. They're how data science gets operationalized. They are how end-users - whether they're subject matter experts, business decision makers, or consumers - interact with data, big and small. We all use a data application when we book a flight, for instance.
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