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October 2016, Volume 4, Number 5

Data visualization techniques, tools at core of advanced analytics

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. 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. 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...

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