The reason why Chemistry fascinated me is the idea of being able to facilitate reactions between substances to create an

I find it interesting to design and create solutions using data and programming alone. Although, like what I said in my first point, I realize how putting my work out there requires a ton more work than I am trained on.

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Unfortunately, many companies don’t realize what it takes to onboard and unleash the full potential of their data scientists. Many companies think of data scientists as jack-of-all-trades and someone that have the powers to take their dirty data and make them millions in return in no time.

Learning web development can allow me to learn the ins and outs of software development in a fun and visual way, while at the same time learning some skills that can be useful in my career as a data scientist.

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I bet things are going to stay the same way as companies are navigating this awkward phase as they figure out the proper way to take advantage of data science, and as the current data scientists specialize in specific data science domains.

At the moment, I desire to be more flexible in terms of my skills and be able to add more value to the company I’m working for. I would like to approach problems in a more agile way by having the know-how from both the data and the dev side.

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Another thing known in the data science community is the curse and blessing of being in an overhyped industry. It’s been touted as the sexiest job of the 21st century for a reason.

Could be. Up until now, I know the definition of DS-related roles is still murky but one thing certain about ML engineers is their superior technical prowess versus your average data scientist. I guess, learning how to deploy web apps and tinker on the back end of stuff is a pathway to having a skillset closer to that of an ML engineer.

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of viral explosion is happening. How high will the numbers rise? A little bit of math — don’t worry, it’s simple math — can tell us a lot, right about now. From the first phase to the second phase, the numbers tripled — 25,000 to 75,000. From the second to the third, they doubled — 75,000 to 150,000. See the trend? Tripling, doubling — the rate of acceleration, the growth exponent, gets smaller. That’s to be expected, in a sense, because even exponential growth can’t keep growing at an accelerating rate forever.

It is a blessing that companies started to realize they lack a crucial capability to unlock insights from data (or could be just plain FOMO?). As a result, job prospects for data scientists and analysts skyrocketed.

Perhaps. However, it is not the only way, as I can also do so by going deep into a problem domain. Nonetheless, I know learning software development is useful either way and can widen my perspective as I’m building my career in the tech industry.

Knowing how powerful data science and AI is — and how widespread it is across industries, I know I might be able to stumble upon some bright idea that I hope I would be able to translate into some actual product myself (or with a team)