In the third grade, my science teacher sent shockwaves when she failed the final projects of more than half the class (thankfully I was in the minority).
This is it??? This is all you have?!
You can do better than this. These are too easy.
Give me something that’s actually worth… something!
Let me remind you: WE WERE THIRD-GRADERS. We were little brats who had never been told we sucked, much less failed.
Stricken by this failure, one classmate approached me after class to ask for advice. He had always been in the top 10 of the class. This must have devastated him.
Too bad I was never good at consoling, even as a kid. So instead I told him a story.
Of how I was playing outdoors the day before and was bothered by mosquitoes. Of how, try as I might, I couldn’t find where my mom hid the insect spray.
So I just used the first thing I found in the kitchen: Maggi savor.
(For those outside the Philippines, maggi savor is a blend of liquid seasoning, something like soy sauce but with garlic and lime.)
And to my surprise it worked. Not as effectively as insect spray, but the mosquitoes no longer buzzed as actively as before.
You can guess what happened next: Classmate wins title of “Best Project” for his study on The feasibility of soy sauce as a mosquito repellent alternative. I was… well, I passed so all was well.
Why am I sharing this story?
Because to me, my experiment had been nothing more but a curious solution to play outdoors.
But to my friend, and to my science teacher, it was a problem worth solving.
And as it turns out, that’s how to become a data scientist.
One of the most popular posts I’ve written on this blog is Getting started with Data Science, for the complete beginner. Its also one of my first posts.
Since then, many articles on the same topic have come up. But of note is this one published in Forbes (originally from Quora). It answers the question, “What’s the best path to becoming a data scientist?”
- Pick a topic you’re passionate or curious about.
- Write the tweet first.
- Do the work.
Where I said have a personal project, the writer took it to the next level by recommending to have a public portfolio:
I recommend building up a public portfolio of simple but interesting projects. You will learn everything you need in the process, perhaps even using all the resources above.
Makes sense right? More and more we’re judged by what we can do, no longer by the credentials we have. Artists, architects, and now programmers and developers… more and more jobs require having a portfolio.
What I haven’t considered is to write the tweet first.
Is the project even worth pursuing?
It sounds obvious, but people are eager to jump into a random tutorial or class to feel productive and soon sink months into a project that is going nowhere.
She’s got a good point though.
So. I now know I have to revisit my projects and write their tweets… but how do I talk about that portfolio?
If you’re like me and data science isn’t your day job, how do you talk about what are, essentially, your side projects?
It’s unfortunate that side projects are often overlooked by the people who aren’t actively working on them. Side projects can be immensely rewarding to talk about. They demonstrate a lot about how you work.
Thankfully LinkedIn has the ability to showcase projects. Its the perfect avenue to showcase your portfolio.
In person though, you may want to try this approach:
- Start with the problem
- Define your approach
- Share the challenges you faced
- End with the results
- Follow-up with what you would do differently
Again, it starts with the problem.
Like most things, the start is the most difficult step.
Finding the right problem is hard. But it might not need to be. It might already be there, right in front of you, just under your nose… and you just haven’t recognized it as a problem yet. Just like maggi savor.
In order to re-course my path to data science, the first thing I’m doing is to take a second look. But this time with a fresh set of eyes.