Nowadays, more than ever before, there are a bunch of experts in the data science field, telling everyone what to think and what’s important. This, although useful to some extent, may be a hindrance after you reach a certain level of expertise. That’s not to say that experts’ views are useless, but it’s always good to take them with a pinch of salt.
Experts are people who have learned the field in such depth that they can think of it as people who speak a foreign language can think in terms of that language’s vocabulary and logical structures (e.g. grammar and syntax). An expert in our field doesn't see data science as something outside himself, but rather as a part of him, much like his ability to read and write. This level of intimacy with the know-how in data science enables him to perceive things that most people cannot, and offer deeper insights about the ins and outs of data science.
However, experts don’t know everything and it’s very easy for someone to become so enticed by his expertise that the boundaries of his understanding become blurred. This is a very dangerous thing, since the expert may have the false impression that he knows everything there is to know and/or that everything he knows is valid. However, data science is a very dynamic field, so even if you attain expertise in it, things change so some adaptation is in order. Some experts forget that.
Even if experts have a lot to teach us, we need to always be aware that there are things they do not know, or that they do not know well enough. For example, many experts are very knowledgeable about traditional statistics and whatever lies beyond that part of data science is secondary for them. Yet, even in the field of statistics they only know what they have learned and may lack the curiosity to explore different kinds of Stats, or the humility to acknowledge their existence. Experts like that will tell you that data science is all about statistics, reiterating the stuff they have learned. However, if you try to pinpoint the limitations of what they know, they will label you as a heretic, which is why most people don’t say anything back to them. This is dangerous though, since silence can strengthen their already inflated view of their authority, and bring about even stronger views in them.
That’s why the best approach is to try things out yourself. An expert makes a claim about a certain topic in data science; instead of taking it as fact, put it to the test it to see if it holds water. If it’s something that’s public knowledge, cross-reference it. If it’s something that can be verified or disproved through experimentation, write a script around it. Whatever the case, don’t take things for granted, just because some expert says so.
All this is related to developing the right mindset for data science, which is all about asking questions and trying to answer them in a methodical manner (aka the scientific method), using a variety of data analytics methods and lots of programming. Techniques and tools become obsolete sooner or later, but this mindset I’m referring to is always relevant…
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.