Data Analyst Nanodegree

The majority of raw data, particularly big data, doesn’t offer a lot of value in its unprocessed state. Of course, by applying the right set of tools, we can pull powerful insights from this stockpile of bits.

In any big data setup, the first step is to capture lots of digital information, “which there’s no shortage of,” said Dr. Michael Wu, chief scientist of San Francisco-based Lithium Technologies, which develops social customer experience management software for businesses.

With data in hand, you can begin doing analytics. But where do you begin? And which type of analytics is most appropriate for your big data environment?

In a phone interview with InformationWeek, Wu explained how descriptive, predictive, and prescriptive analytics differ, and how they provide value to organizations.

“Once you have enough data, you start to see patterns,” he said. “You can build a model of how these data work. Once you build a model, you can predict.”

Big Data Analytics

The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.

The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

Exploratory data analysis

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Why data analysis is Important

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Online data streaming?

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Too Much Data?

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New Data Analytics tools

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