As experience and knowledge accumulate in our minds, it’s increasingly easy to lose touch of that original spark that brought us into this journey of learning, in the fascinating field of data science. I’m referring to that sense of wonder that made all this otherwise dry know-how of math, programming, and data, something we could lose sleep over. Because if you are really in a state of wonder, it’s easy to forget to eat, postpone other tasks, and even find sleep somewhat less important, when your other option is delving more into the learning of the craft.
A sense of wonder, however, is much more than curiosity or even interest in data science. It is all that, but it’s also a way of feeling, a higher sentiment if you will. Being at wonder is what incites wondering and going into more depth. It is what makes a seemingly mundane task, such as data cleaning, appear intriguing and valuable. It is what makes learning about a new model something truly interesting, not just as a memory-based activity, but also as something that sparks imagination and innovation. It is wonder that makes us ask “what if?” instead of just being content with what is presented to us.
Naturally, this sense of wonder is fleeting, just like the perspective we have as newcomers to data science. The more we learn, the more limited our wanderings in the vast knowledge that the field entails, since being more focused on specific tasks and time frames are of the essence. That’s normal since as data scientists we need to be practical and akin to the way the world works, otherwise, we’d be unemployable. Yet, at a certain point of aptitude and understanding of the craft, it is this sense of wonder that enables us to go further and grow beyond what we are expected to be.
The sense of wonder can be cultivated through a sincere wish to become better for the sake of being better, a wish nourished by our love for data science. Ambition can only take us so far, plus after a while, it can become stressful. Wanting to become better because of a lasting motivation is therefore essential for bringing about the sense of wonder. However, we also need to make time for it and allocate resources to such endeavors. Learning through a book or a crash course may be efficient but it’s what we do beyond this that enables us to learn deeply and cultivate the sense of wonder. Liaising with people who already have this sense strong in them, such as beginners who are dedicated learners of the craft, can be a great aid too. Finally, we need to think about the craft and experiment with new ideas. If we just rely on what this or the other expert says, we are bound to be limited by them. We need to study existing ideas, but also dare to venture beyond them, exploring new models and new metrics. Most of them are bound to yield nowhere but some of them are bound to work and help us look at data science from a different angle.
Cultivating a sense of wonder isn’t easy and it’s an ongoing challenge. However, through it, new perspectives come about (such as some of the stuff I talk about in this blog periodically) while the connectedness of the various aspects of the field becomes apparent. All in all, it’s this perspective that makes the field truly wonderful, much more than a line of work. That’s something to wonder about...
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.