Data science is the result of a new paradigm taking place in IT. The question was raised recently, and here I explain how and why data science is part of this new paradigm, and not recycled material.
New arsenal of techniques and metrics
Many data science techniques are very different, if not the opposite of old techniques that were designed to be implemented on abacus, rather than computers. These new tools are often model-free.
Indeed, old techniques such as logistic regression and classification trees don't even belong to data science, more stable techniques are used in data science. You can find many of them published as open intellectual property, in our data science research lab.
The way (big) data is processed has also dramatically changed: it requires optimizing complex Hadoop-like architectures, and computational complexity is not an issue any more in many cases (as long as you use efficient algorithms). It's the time that it takes for data to flow back and forth in data pipeline systems, that is now the bottleneck.
Saying that data science is not creating a new paradigm shift, is like saying that if we claim Earth rotates around the sun rather than the other way around, there's no change in paradigm, because after all, we are still dealing with 2 celestial bodies and 1 rotation - nothing changed.
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