Why Data Science is Hard

- 1 min

After a data visualization workshop at the NUS datathon someone came up to me and asked:

“I couldn’t learn any of this stuff from online tutorials—how do you make it seem so easy?”

After two semesters in the deep end of data science after my biochemistry degree, I’ve echoed her sentiment from time to time. I wrote this blog post to say one thing: data science is difficult. Don’t let others tell you otherwise.

While this isn’t necessarily a fresh take or earth shattering news, I think it’s important to reiterate for beginners and veterans alike. It’s intimidating to hear people use Tensorflow vocabulary, but I can guarantee you none of those people knew what linear regression was from day one. Regardless of your SAT score, previous job, or experience with a computer—if you put in the work and are genuinely curious this field can get you far.

I think machine learning has a steep learning curve full of confusing terms to keep interested learners away and data scientists expensive, scarce, and employed.

But I’m also here to say that it’s worth it: any discipline, problem, or person could benefit from your skills. The data science tool belt equips you with the most state of the art weapon of mass destruction for change. The beauty of this whole process is that you’re obligated to choose how you want to make that change. From finance to forecasting, retail to robots, and most things in between: machine learning is clearly here to stay.

So next time you’re struggling through an R package or trying to understand what the fuck a perceptron actually is, make sure to take a step back and realize that you’re surfing a huge, interdisciplinary, accelerating tsunami of information.

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