How to build code you can be proud of as a data scientist. Here are some software engineering principals to get you writing clean, readable and easy to work with code as a data scientist.
It is pretty much a stereotype that data scientists can’t write clean and understandable code. This doesn’t have to be the case for you. By learning a few principles of how to write code properly, you can use the stereotype to your advantage and set yourself apart from the competition.
To find the best practices, we should look no further than the reliable practice of software engineering. Here are some software engineering principals to get you writing clean, readable and easy to work with code as a data scientist:
When it comes to working with new technologies, one of the biggest mistakes I see data scientists make is to ignore the documentation. The documentation of a tool or library is solely prepared to help users have an easier time applying the tool to their work.
When users fail to consult the documentation, the development phase could take too long or the library might not be used to its full potential.
So when you’re about to use a tool you never used before, make sure to read and understand the documentation. It will make your life easier.
While we’re on the topic, I also have to emphasise the importance of writing documentation. You might not be developing state of the art code that will be used by thousands of people, but simply noting down what everything does in your code will be good enough.
It will be useful for people who will use your code after you, your teammates who will build upon your code and also for you when will look back at what you write a couple of years down the road.
Your documentation doesn’t have to look super professional. Just a word document describing what this piece of code does, what the input is and what the output is would be good enough.
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