Deep Blue Beats Kasparov: Why I’m learning data science

I’m old enough to remember when we thought that no computer would ever truly master the game of chess. We believed that the intricacies of the game and the subtleties of sacrifice were beyond a machine. In 1996, Gary Kasparov proved us right. A solid 4–2 victory (really 3–1 with 2 draws) by Kasparov was undeniable proof that the human mind was the superior processor. Isaac Asimov’s and Philip K Dick’s fictional visions of the future, where robots and androids threatened the existence of mankind, were just interesting bits of reading and nothing more.

And then, 1 year later, the rematch happened. And Kasparov lost. My 12 year old brain conjured visions of Skynet, and HAL, and Replicas and every other scary AI apocalypse I’d ever read or seen on a screen. If the best of us could be beat, what were our chances of survival? Luckily, I was talked down by my father, who reminded me that humans always came out on top in those stories. What preteen would need more comfort than that.

Fast forward 23 years and cars are driving themselves, Pandora knows my taste better than I do, and Facebook and Google…are Facebook and Google. Machine learning is absolutely, undeniably, and undisputedly the future. And since I would like to be a part of the future, I’ve decided to take the first steps to becoming a Data Scientist.

Right now, I’m learning to code in Python, discovering what Jupyter and Google Collaborative notebooks are. I’ve floundered around with python libraries, struggled with the silliest syntax errors imaginable, and felt the joy of completing my first assignment. I’ve barely scratched the surface of what I need to know, just to start learning about Machine learning, but I’m excited and can’t wait to dig further. The Future looks bright.