The R programming language is pervasive in academia as a statistical tool, but it’s quickly becoming a critical language for companies that want to leverage the power of predictive analytics.
Roughly 70 percent of data miners report using R and some 25 percent use it as their primary tool. The R language excels at including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, and clustering.
But businesses that want to get a handle on big data and fast data face some challenges in using open-source R, including memory limitations and lack of scalability.
In this white paper, you will learn:
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What the key success factors are for enterprise analytics.
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Three requirements for deploying predictive analytics.
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The most popular R algorithms in use.
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Overcoming open-source R challenges by using a commercial platform such as Teradata Aster Analytics.
