The key word in “Data Science” is not Data, it is Science

What does &quot;science&quot; mean in this context anyway? An astronomer, say, can work with vast, vast swaths of data but that doesn&#x27;t make the astronomer a &quot;data scientist&quot;. Statistics has some fairly generic tools but it doesn&#x27;t seem to me these add up to science in the sense that fields like chemistry, biology, geology and physics are sciences.<p>My modest exposure to machine learning at the professional level gives me impression that the &quot;real experts&quot; combine a strong mathematical understanding, long experience and some good rules of thumb to perform better than a grad student shooting in the dark, <i>if</i> they happen to perform better.<p>Oddly enough, all articles about how hard it is to become a &quot;real data scientist&quot; gives the impression that however much expertise is involved, that expertise isn&#x27;t the codified understanding that is &quot;real science&quot; - even a physics undergraduate does real physics because scientists, physics codified their methods.<p>Maybe &quot;data science&quot; can become science. But suspect that what will become scientific is the understand of whatever entity is producing the data. Which isn&#x27;t to discount the learning of experts here but simply to note that compendiums of rules-of-thumb and feelings indicate what Thomas Kuhn might a pre-scientific field.

Data science is one of those words that are like a semantic mine, which HN seems to be especially susceptible to. These words (a few years ago it was &quot;cloud&quot; or &quot;Web 2.0&quot;) catch on because they capture a bunch of things that have chained which seem related. But, they aren&#x27;t clearly definable. This traps people (I think HN people especially)<p>Web 2.0 was a trend in prominent personality types, types of websites, business models, increased scale of online interaction, use of real names, web design styles, programming languages. It wouldn&#x27;t have been absurd to see a guy with a specific look and a specific laptop and say &quot;that&#x27;s so web 2.0&quot;. To add insult to injury, pretty much all the stuff that gets captured in a word like web 2.0 has predecessors. Crowd sourcing? What about Wikipedia?<p>I think a good analogy is &quot;movement&quot; in art, philosophy &amp; culture. Modernist is a word that encompasses Frank Lloyd Write, Pablo Picasso, James Joyce, Ayne Rand &amp; karl Marx. It applies to paintings, manifestos, econometrics and buildings with straight lines.<p>That&#x27;s the kind of word that &#x27;data science&#x27; is. We found ourselves recording a lot of data as a sort of side effect of digitization. It&#x27;s growing. Then we start to try and get some value from that data. Some new stuff is possible with that volume of data. Some new people are now interested in data. A lot of the tools people were using to collect and analyze data don&#x27;t work at that volume, so we start using new tools. We end up with a word that includes astronomers, netflix, medical researchers, self driving cars, R, statistical theories etc.<p>Data science doesn&#x27;t mean anything that specific yet. It&#x27;s best not to lead the discussion (as I am doing right now) to a discussion about the word, what qualifies as data science.

Data gives a perception only when there is a problem to solve and you need support from the data. Problem solving is more about the knowledge in the vertical, the approach to the problem and your capability to think from multiple angles, which does not have to do about science.<p>Good article, nevertheless.

I really love all these articles about Data Science, but it&#x27;s a lot more than statistics. It&#x27;s programming, it&#x27;s domain knowledge, and yes, it&#x27;s a lot about thinking of the meaning, format, and pliability of the data.

By what process can I &quot;understand whether these correlations matter for specific, interesting questions&quot;?