Python for Data Science Lecture 29 || OOPs (Object-Oriented Programming) Part 2

Welcome back to our Python for Data Science series! In Lecture 29, we continue exploring Object-Oriented Programming (OOP) in Python, diving deeper into advanced concepts and practical applications.

🔍 Inheritance: Learn how to use inheritance to create a hierarchy of classes and share functionality between them.

🛠️ Polymorphism: Discover the power of polymorphism and how it allows different classes to be treated as instances of the same class through common interfaces.

📊 Method Overriding: Understand method overriding and how to modify or extend the behavior of inherited methods.

💻 Practical Examples: Follow along with real-world examples to see how these OOP concepts can be applied in data science projects.

📝 Best Practices: Gain insights into best practices for using OOP in Python, making your code more modular, reusable, and easier to maintain.

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By the end of this lecture, you'll have a strong grasp of OOP principles and how to effectively apply them in your data science work. Don't forget to like, comment, and subscribe to keep up with the rest of the Python for Data Science series!

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