data science, statistics

Putting the science in data science

Data science is not just overhyped marketing BS, at least not if you are doing it right.

Owning up to the title of data scientist [Sean McClure | Data Science Central]:

To own up to the title of data scientist means practitioners, vendors and organizations must be held accountable to using the term science, just as is expected from every other scientific discipline. What makes science such a powerful approach to discovery and prediction is the fact that its definition is fully independent of human concerns. Yes, we apply science to the areas we are interested in, and are not immune to bias and even falsification of results. But these deviations of the practice do not survive the scientific approach. They are weeded out by the self-consistent and testable mechanisms that underly the scientific method. There is a natural momentum to science that self-corrects and its ability to do this is fully understandable because what survives is the truth. The truth, whether inline with our wishes or not, is simply the way the world works.

Opinions, tools of the trade, programing languages and ‘best’ practices come and go, but what alway survives is the underlying truth that governs how complex systems operate. That ‘thing’ that does work in real world settings. That concept that does explain the behavior with enough predictive accuracy to solve challenges and help organizations compete. This requires discovery; not engineered systems, business acumen, or vendor software. Those toolsets and approaches are only as powerful as the science that drives their execution and provides them their modeled behavior. It is not a product that defines data science, but an intangible ability to conduct quality research that turns raw resources into usable technology.

Why are we doing this? To make our software better – to help it learn about the world and then, based on that learning, improve business outcomes:

The software of tomorrow isn’t programming ‘simple’ logic into machines to produce some automated output. It is using probabilistic approaches and numerical and statistical methods to ‘learn’ the behavior and act accordingly. The software of tomorrow is aware of the market in which it operates and takes actions that are inline with the models sitting under its hood; models that have been built from intense research on some underlying phenomenon that the software interacts with. Science is now being called upon to be a directly-involved piece of real-world products and for that reason, like never before in history, the demand for ushering in science to help enterprise compete is exploding.

Any time someone equates data science with storytelling I get worked up. Science is not storytelling and neither is data science. There is science to figuring out how the world works and how to make things better based on knowing how it works.

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