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Far from being a bit player in analytics applications, data visualization fills several crucial roles throughout the process.
From initial data exploration to development of predictive models to reporting on the analytical findings the models produce, data visualization techniques and software are key components of the data scientist's toolkit.
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The data scientists managed by Zhao build recommendation engines, perform A/B tests of new webpage layouts and help the marketing team plan and execute targeted email campaigns.
Without them, analytics teams are engaging in a nearly impossible task that's tantamount to flying an airplane while blindfolded.
"Data visualization just makes our analyses so much more efficient," said Daqing Zhao, director of advanced analytics at The only way to see patterns is to use your eyes." The advanced analytics team at -- the San Francisco-based online arm of retailer Macy's Inc.
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Predictive modeling and other kinds of advanced analytics are done with powerful software built specifically for running complex algorithms on large data sets, such as programming languages like R and Python and analytics tools like SAS and IBM SPSS.
But many data scientists and analytics managers will tell you that a lot of their work -- and ultimately their ability to provide useful information to business executives -- also relies heavily on more humble data visualization tools.
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