Throughout our careers in data science and AI, we constantly encounter all sorts of obstacles that hinder our development. This is something inevitable, particularly when we undertake a role that's constantly evolving. However, the biggest obstacle is not something external, as one might think, but something closer to home. On the bright side, this means that it’s more within our control than anything subject to external circumstances. Let’s clarify.
The biggest obstacle is related to the limits of our aptitude, something primarily linked to our knowledge and know-how. After all, no one knows all there is to know on a subject so broad as data science (or AI). However, as we gather enough knowledge to do what we are asked to, we are overwhelmed by the idea that we know enough. Eventually, this can morph into a conviction and even expand, letting us cultivate the illusion that we know everything there is to know in our field. Naturally, nothing could be further from the truth since even a unicorn data scientist has gaps in her knowledge.
One great way to avoid this obstacle is to constantly challenge yourself in anything related to our field. I'm not talking about Kaggle competitions and other trivial things like that. After all, these are hardly as realistic as data science challenges. I'm referring to challenges to techniques and methods that you are lacking as well as refining those that you already have under your belt. This may seem simple but it's not, especially since no one enjoys becoming aware of the things he doesn't know or doesn't know fully. Perhaps that’s why developing ourselves isn’t something easy or popular.
Another way to enhance ourselves is through reading technical books related to our field. Of course, not all such books are worth your while, but if you know where to look, it's not as challenging a task. What's more, it's good to remember that the value of such a book also depends on how you process this new information. For example, in many such books, there are exercises and problems that the reader is asked to solve. By taking advantage of such opportunities, you can learn the new material better and grow a deeper understanding of the topics presented.
One way for learning more is through Technics Publications books. Although many of the books from that publishing house are related to data modeling, there are a few data science-related ones as well as a couple on AI. Of course, even the data modeling books can be useful to a data scientist, since we often need to deal with databases, particularly in the initial stages of a project. Also, if you were to buy a book from this publisher using the coupon code DSML, you can get a 20% discount. The same applies to any webinars you may register for. So, if the cost of this material is an obstacle for you, at least with this code you can alleviate it and get a bigger bang for your buck!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.