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Visual discovery in data science plays a crucial role in testing hypotheses and mining for patterns, trends, and anomalies. It is particularly useful when dealing with large and complex data sets, where the initial knowledge about the data is limited. The process is more open-ended, allowing for a flexible approach to data analysis.
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Visual discovery is the most complicated quadrant because it consists of two categories: testing a hypothesis and mining for patterns, trends and anomalies. Berinato says: "The former is focused, whereas the latter is more flexible. The bigger and more complex the data, and the less you know going in, the more open-ended the work."
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