Object-Oriented Programming for Data Scientists

Object-Oriented Programming for Data Scientists

Object-Oriented Programming (OOP) for Data Scientists. The goal of OOP is to organize code in a way that makes it maintainable, easy to read, and above all else, reusable. Object oriented programming allows for parallel development between multiple data scientists who want to work with the same codebase for their projects. Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator.

With so much to learn in the way of programming, data analysis, machine learning, artificial intelligence, mathematics, and all of the many other components of data science, it’s fair to say that the learning of concepts becomes an arduous affair when becoming a data scientist.

With data scientists coming from a multitude of backgrounds, many of them not computer science-based, it’s completely understandable that some computer science principles are passed over in favor of getting to the good stuff: completing data analyses.

One of those concepts is object-oriented programming (OOP).

When you ask current data scientists for their opinion on OOP, you’ll probably come back with a mixed bag of answers. In some cases, OOP can be incredibly instrumental in reducing the complexity and time it takes to complete an analysis. In others, OOP can result in having more code than you need or even know what to do with.

Depending on your situation or the project you’re working on, you may find it beneficial to switch to an OOP approach, or you may find that it hinders your progress.

However, one thing is certain: as a data scientist, it never hurts to have an extra skill in your back pocket that may come in handy when you least expect it. In other words, why not learn a little about OOP and see how you or your team can benefit from its principles?

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