Data Scientist: Owning Up to the Title

Data Scientist: Owning Up to the Title

Turning something raw into something industrially valuable has always required 2 things; science and engineering. The science is our attempt to explain and predict the behavior exhibited by some complex system and capture those explanations in the form of testable models. The engineering looks to mechanize those modeled concepts into useable tools that make a direct impact on society.

As we move into the information age our definition of what we consider valuable is shifting to something more intangible. The industrial revolution showed us how machines could take over menial and repetitive tasks and elevate society to better standards of living. But in the information age the bottleneck to productivity is no longer inadequate assembly lines or slow equipment. We now look to machines to produce something that is less physical and more information-driven. This is because society has extended its human nature beyond its physical limitations where we now communicate on the planetary scale. Where we gain knowledge of our world through digitally-curated and organized news feeds. Where we promote our ideas through online videos and blogs. Where we run our financial institutions, our governments, our hospitals, our schools and our businesses using collected and communicated data.

Producing something of value now requires the ability to move from large amounts of raw collected data to something that elevates our standard of living by allowing us to use that information in new and effective ways. But this requires more than simply scaling our existing machines into this new world of rich and varied data. This new world represents entirely novel complex systems that we need to understand. In order to produce value we need models of its behavior so we can capture those new-found concepts in our machines. If we want to turn this new "oil" into something useful we can't just find better ways of digging it out of the ground. We need to understand what makes this oil exhibit its behavior so we can mechanize that knowledge and produce real value. This requires science.

Enter the Data Scientist; a new kind of scientist charged with understanding these new complex systems being generated at scale and translating that understanding into useable tools. Virtually every domain, from particle physics to medicine, now looks at modeling complex data to make our discoveries and produce new value in that field. From traditional sciences to business enterprise, we are realizing that moving from the "oil" to the "car", will require real science to understand these phenomena and solve today's biggest challenges.

Unfortunately, along with this increased demand for 'science on data' is an accompanying ambiguity with regards to what it means to be a data scientist. If you peruse LinkedIn you'll see heated debates about the term and articles on its meaning. Is it statistics, mathematics, computer science, machine learning, artificial intelligence, or just theoretical physicists applying their skills to the real world? You'll get someone in every crowd that throws up the air quotes around 'science' proudly informing the masses that it's all hype, and that it will pass soon enough (interestingly, these people are never scientists). As the demand for understanding our data rises, we see universities offering courses, organizations offering programs and certificates, all in an attempt to fill the skills gap. Compounding the problem is the fact that eager students looking to jump on the data science bandwagon are as diverse as particle physicists to MBA graduates, increasing the risk that the domain will be diluted with such a range of skills that it becomes challenging to pin down what a data science resume actually looks like.

So we can turn to some of the popularizers of the term like Jeff Hammerbacher and try to get clarification, but when and why a term was coined is independent of what the title actually represents today.

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