From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

Visualizing data

From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

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Visualizing data

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- Data visualizations enable us to visually communicate trends, groups, and key outliers or anomalies in data, much like the intended outputs for algorithms for machine learning and AI. But remember, just because you can do it in Python doesn't mean you should. Visuals are a great way to better understand algorithm outputs, which include finding trends, groups, or anomalies and outliers. There are loads of visuals out there that we can tap into, but I find there are three key visuals that I use most of the time when I can. Bar charts, for example, can help us identify the largest group or groups in our data line. Line charts can help us understand trends over time, for example, and scatter plots can help us identify groups, and they can also help us identify outliers and anomalies in our data. When we create visuals, we can use the outputs, whether they involve using Python or not, to build visuals. My own general guideline is to use the built-in Excel chart options when I can, and to tap into Python code to build visuals when Excel doesn't offer the visuals that we need. Visuals can help us understand more about the data. They can also help us communicate the results to others. They can be tied directly to an algorithm, like a Dentro grand visual, or they can visualize the results of the algorithm through a known visual, like adding anomalies to a line chart.

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