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Data Visualization for Scientific Discovery


Choosing the visual form for a visualization is a decision about what aspects of the data matter most. Highlight or ignore outliers? Look at values, differences, or changes? Compare to 0, median, or mean? In scientific analysis we risk missing discoveries by failing to notice important features of our data, yet we often use default parameters and charts without realizing what we might miss. I will demonstrate how to translate questions about your data into chart parameters. Using Python examples, I'll illustrate powerful techniques like using color intentionally and creating 'small multiples' of charts that vary visual form or data.Presenter(s): Speaker: Zan Armstrong, Google


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