Data Science vs. Data Analytics

December 12, 2012 Paul M. Davis

Image by Dmitry Baranovskiy via Flickr. (CC BY 2.0)

With Big Data becoming one of the year’s top buzzwords, terms describing the practitioners working with said data can become confused. As Data Scientists grow in demand in the job market, it’s important that the role doesn’t grow conflated with that of Data Analytics. In a recent blog post, Data Architect Venu Anuganti clarifies some of the distinctions between Data Scientist and Data Analytics engineer.

“Data Analytics (DA) in general is a logical extension (or just a buzz word) to Data Warehousing (DW), Business Intelligence (BI); which provides complete insights into business data in most usable form,” Anuganti writes. “Every business who deals with ‘data’, must have ‘Data Analytics’; without analytics in-place; the business is treated as dead man walking without a heart, a soul and a mind.” On the other hand, Anuganti defines a Data Scientist as having a scientific research background, who is capable of yielding deep predictive insight from massive data sets.

Ideally, Data Science and Data Analytics will be performed in concert with one another, Anuganti states. “Data science is a data consumer within the business unit and solely depends on data provided by data analytics team,” he writes. “More than that; most of the model predictions or algorithms works really well on large data sets due to better probability on bigger data sets ; so the bigger the data; you have much better chance to predict it right and drive the business further; which means both are directly depending on each other.”

Read more of Anuganti’s distinctions between Data Science and Data Analytics at his blog.

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