The Hard Truth: Data Science Isn’t for Everyone*Opdiee2PNe751uSP

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Lack of knowledge — Unused potential

The field of data science has become incredibly attractive because people have the illusion that it’s an easy-to-enter and high-reward field. Harvard Business Review called it the “sexiest job of the 21st century.” Dozens of online courses promise that only a few months of hard study can prepare anyone to land a data scientist position at a decent company. My personal experience further reinforces this notion: I started studying AI in September 2017 and got a job at an AI startup in January 2018. Four months of study seems very little time to learn enough of a field to land a job.

What solves this apparent incoherence is that there are different approaches to learn anything. If I had started studying AI theory since Turing and the early ideas of symbolic AI, I’d still be learning expert systems 4 years later. Instead, I took a brief course to get some basic knowledge on machine learning and then went directly to learn deep learning and coding. I used a top-down approach: Instead of setting the bases firmly, I learned by playing with the most immediate applications I could find.

However, this approach isn’t risk-free. If a person without a good math/statistical background — my example isn’t valid because I was an aerospace engineering undergrad — thinks that learning Python, TensorFlow, and some key architectures and models is enough to work in AI, they’d be strongly mistaken.

Recently, some articles about this topic — expressing diverse opinions — have been published in Towards Data Science. Soner Yıldırım wrote a popular post last month stating that a data scientist should know how to do the tasks of a data engineer. Earlier this week, Chris The Data Guy wrote a controversial article stating that an ML engineer doesn’t need math. And two days ago, Sarem Seitz published a piece arguing the exact opposite.

There’s always a trade-off. If we choose to spend more time coding and doing real projects, our theoretical understanding would be more shallow. If we choose to learn the groundwork and get in touch with every theory, algorithm, and technique, our practical skills would be more shallow. Whatever you do, if you don’t dedicate enough time to learn about a field, part of your potential would be unused. Your education would be only half-complete which could jeopardize your options to get a data science/AI job.


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