How to Make Other Data Scientists Drool with Envy
If you are a data scientist, there is a skill set you can learn that is transformative.
Doors will open for you, wherever you go. Doors your colleagues can’t even see.
It will launch you like a rocket to a new level, where what you accomplish with ease makes others drool with envy.
And the best part: once you really learn it, you have it for life.
What is this skill set, you ask anxiously, from the edge of your seat?
Software engineering skills.
Add this to your almighty data science skill set, and there will be nothing to stop you. I’m not just talking about becoming a data engineer or a type B DS. Even if you want to remain a normal type A data scientist for analysts, learning this skill set allows you to run happy emoji laps around scientists from crying emoji data that don’t.
So how do you do that? Some of the keys to this kingdom:
1) Escape from the notebook
You’re going to hate this one:
You need to become EXCELLENT to write code OUTSIDE of notebooks.
Yes, I know you love Jupyter. It’s fantastic. Nothing against.
But you can only get so far in that playpen.
If you want to write functions, classes, and modules that OTHER data scientists import into THEIR notebooks …
Develop systems that take advantage of the work that other data scientists are doing, at a higher level …
Or even make your brilliant knowledge usable by people who don’t read math books for fun …
You cannot do any of these things in notebooks. Not in a remotely effective way.
It’s time to get ready with more sophisticated software engineering tools and practices.
2) Object-oriented master programming
It’s strange how bad most data scientists are at this.
OOP is much more important than you think. It is the foundation for everything else you do when writing complex and powerful software systems.
When you import a DataFrame from Pandas … that’s a class.
When you create a LogisticRegression classifier in scikit-learn … that’s a class too.
You are USING classes all day, every day. Type B data scientists created them for you to use.
But that only scratches the surface. NOTHING will elevate you and differentiate you from other data analysts, like learning to write good object-oriented code.
3) Learn to write unit tests
Well, except maybe writing unit tests.
This is a GREAT deal. The libraries you trust every day use automated testing. They use a lot of them. That should tell you something.
Writing automated tests and doing test-driven development … it’s a SUPER POWER. Change completely what you are capable of. When you learn to write tests, you can suddenly accomplish things that you couldn’t even touch before. Especially when combined with your OOP skills. Do you see how they build each other?