People talk a lot these days about what it takes to be a good data scientist and how if you do their boot camp or join their course you will acquire that and make yourself stand out from the data scientist pool. Some of these people may be on to something but they generally focus a lot of specific skills and general abilities. That’s fine if you have the time to study what they are saying and find for yourself what you need. However, if you just want a single idea that is in the root of all the stuff they talk about, that’s something few can share with you, because they probably don’t know.
There are data scientists know, however, what it takes to be a good data scientist and many of them have already embodied this in their careers. Yet, they are so busy applying this that they don’t go out of their way to let you know, unless of course they are into education, in which case they will probably mention it in their books or videos.
One feature that I’ve found it succinctly summarizes what it takes to be a good data scientist, regardless of your domain or your specialization, is persistent engagement in the craft. Let’s break this down a bit, since it’s a fairly complex feature (a meta-feature if you will). This comprises of two things working in tandem: persistence and engagement. The first has to do with a sense of rhythm and commitment. All decent data scientists are very focused on what they are doing, even if they are involved in other things (e.g. 90-95% of my work is around data science, though I’m also involved in Cyber Security and to a smaller extent, in Neuroscience). Also, we tend to practice data science in one way or another very regularly. In other words, it is part of our daily routine. That’s all manifestations of consistency.
As for engagement, that is more of an inner state, an aspect of the mindset of a good data scientist. It involves being fascinated by the craft, even if it may seem that it doesn’t have any secrets from you any more. The thing is that there are always new things to learn, especially over time as it evolves and new methods and techniques come about. Engagement is akin to what is known in Zen as the “beginner’s mind” which is a certain approach to things as if they are completely new to you. Coupled with the experience and expertise that a good data scientist has, this approach allows him to go more in depth regarding the field and find new ways to bring about value through data science. It also involves coming up with new models, new processes for data engineering, and in some cases, new data products.
Consistent engagement in data science doesn’t require particular talent or experience, however. Everyone can (and ought to) embrace it. So, instead of trying to memorize the inner workings of some obscure model, just because someone else says so, try cultivating this trait first. Afterwards, everything else will appear easier and more interesting, just like new know-how appears intriguing and within reach, to a novice that has a genuine thirst for learning. After all, there are many ways to achieve mastery of the craft, but they all go through consistent engagement.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.