Big Data needs Data Science

We’ve been hosting a series of webinars focusing on how to make Apache Hadoop a viable enterprise platform that powers modern data architectures.

Implementing modern data architecture with Hadoop means that it must deeply integrate with existing technologies, leverage existing skills and investments and provide key services. This guest post from David Smith, Vice President of Marketing and Community at Revolution Analytics, shares his perspective on the role of a Data Scientists in a Big Data world.

While many companies today have not figured out the process of efficiently collecting and storing data with Hadoop, the next step in that process — getting value out the data — isn’t yet in everyone’s grasp. But that’s where Data Science — the process of understanding, analyzing and creating actionable insights from data — comes in. You can use Data Science  to measure regional market shareunderstand consumer sentimentpredict elections, and even make better wine. If you’re new to the concepts of Data Science, here are some resources to help you catch up:

If you’d like to learn more about the data scientist’s toolkit, you might want to view my 2012 talk “The Rise of Data Science in the Age of Big Data Analytics“.

And don’t forget to join the new webinar The Modern Data Architecture for Predictive Analytics with Revolution Analytics and Hortonworks Data Platform on Tuesday to learn how R and Hadoop form part of a data scientist’s architecture.

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