Do Data Scientist Use Object Oriented Programming?

It is estimated that there are 2.5 quintillion bytes of data produced every day in our world. In this data-driven world, the career opportunities for a skilled data scientist are endless. With the data production rate predicted to go higher than of now, the career opportunities for those who can manage data are not going anywhere. This article discusses whether data scientists are using Object-Oriented Programming. Let's find out.

What is Object-Oriented Programming
Object-Oriented Programming or OOP is a model of the programming language organized around objects rather than the actions. It also emphasizes data rather than the logic. Traditionally, a program is considered to be a logical procedure that converts input data into output.

In such cases, the challenge was to come up with a logic that works. The OOP model redefined that concept. It takes the view that we should care more about the objects we are trying to manipulate rather than the logic we use. These objects could be anything from humans defined by names and addresses to little widgets such as buttons on the desktop.

The main advantages of OOP are:
• Programs with a clearer modular structure.
• Codes are reusable through inheritance.
• Flexibility through polymorphism.
• Very effective problem-solving.

Object-Oriented Programming in Data Science
Using Object-Oriented Programming for data science may not always be the best choice. As we said, the OOP model cares more about the objects than the logic. This type of approach is most suited for GUI, interactive application, and APIs exposing mutable situations. When it comes to data science, functional programming is preferred more due to superior performance than compared to the OOP model. The advantage of better maintainability offered by OOP is sacrificed in the data science for the sake of performance.

Polymorphism is an important feature of OOP. It allows a loosely coupled architecture, where the same interface can be easily substituted for different implementations. This feature is very helpful when dealing with applications of large size. However, data scientists seldom use large codebase. They always use small scripts and prototypes. So, OOP would be far too much overhead with no significant benefits.

Although, machine learning libraries are a must needed thing for data scientists. Most of these libraries make use of object-oriented programming, at least the ones in Python. Machine learning libraries such as Scikit-learn heavily make use of OPP. Data scientists who work with R and SQL will never have to use OOP.

It is clear that even though Object-Oriented Programming Offers a lot of benefits, it is not exactly what data science need. So in general, object-oriented programming is seldom used by the data scientists.

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