Python for Data Science Lecture 28 || OOPs (Object-Oriented Programming) Part 1

Welcome to Lecture 28 of our Python for Data Science series! In this session, we dive into Object-Oriented Programming (OOP) concepts, a fundamental aspect of Python that's crucial for efficient coding and data management.
🔍 Introduction to OOP: Understand the basics of Object-Oriented Programming and why it's essential for data science.
🛠️ Key Concepts: Learn about key OOP concepts such as classes, objects, methods, and attributes.
💻 Creating Classes and Objects: Follow along as we create our first classes and objects in Python, demonstrating how to structure data and functionality.
📊 Encapsulation and Abstraction: Discover how encapsulation and abstraction help in organizing code and hiding complexity.
📝 Practical Examples: See real-world examples of how OOP can be used in data science projects to manage and manipulate data.
🔗 Advantages of OOP in Data Science: Explore the benefits of using OOP, including code reusability, modularity, and ease of maintenance.
✨Lecture 28 Notes - www.theiscale....
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By the end of this lecture, you'll have a solid foundation in the principles of Object-Oriented Programming and how to apply them in Python. Be sure to like, comment, and subscribe to stay updated with our Python for Data Science series!
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