What makes a great data scientist?
What’s going to turn you from a good data scientist into a great data scientist? It’s not just learning the latest deep learning library. It’s not debating Python vs. R for the nth time. It’s not putting your hat in the ring for the great notebook debate. I’ll give you a hint: take a look at your data science heroes. What do they all have in common?
No, Perseverance isn’t the latest machine learning library from Facebook/Google/Uber/AirBnb that they haven’t told you about. I mean the personal quality of perseverance.
Why? There’s is always something new to learn, and the new things to learn are coming faster than ever. Plus there’s more data scientists than ever. Why would the company you admire hire you over hundreds of others? One reason they might is that you kept going. Because you learned the latest library and solved a problem with it. You brushed up on logistic regression because the fundamentals are important, too. You followed through and completed that cool pet project that only a handful of people found interesting.
Good data scientists might think they’re doing this. They might have more tweets than you. They might have published more medium posts. They might already have the job at the company you admire. But they’re only good.
The great data scientists keep going. They realized their past work could have been better, so they improved on it for version two. They refactored their code with a new version of that library, despite having to adapt to all the breaking changes. They learned a skill that complimented their technical knowledge, like communication, or leadership, or visualization.
It’s hard to keep going. But there’s no other option. Perseverance is what will move you from good to great.
By: Peter Baumgartner