Object-Oriented Programming (OOP) is a programming paradigm that allows us to organize code into objects that contain both data and behavior. OOP can be used in data science to create classes that represent data structures and perform data-related operations.
Here are some ways in which OOP can be used in data science:
Creating custom data structures: OOP allows data scientists to create custom data structures that fit their specific needs. For example, a class can be created to represent a dataset, with attributes such as the number of rows and columns, and methods to manipulate the data, such as filtering or sorting.
Encapsulation: Encapsulation is the concept of hiding the internal implementation details of a class from the outside world, and only exposing the necessary methods and properties. This can be useful in data science when dealing with sensitive data, where we want to limit access to certain properties and methods.
Inheritance: Inheritance allows us to create new classes based on existing ones, inheriting all the properties and methods of the parent class. This can be useful in data science when working with similar datasets or data-related operations.
Polymorphism: Polymorphism allows objects to take on multiple forms, depending on the context. In data science, this can be useful when working with different data types or formats, allowing us to write code that can handle different types of data in a flexible way.
Data Science Classes in Pune