4. OOPs in Data Science Training – Day 3 ( Nepali Explanation)

Today we took our Data Science journey to the next level! 🚀
In this session, we dived into Object-Oriented Programming (OOP) in Python — one of the most powerful paradigms used in real-world Data Science, Machine Learning, and Software Development. 📚
Topics Covered on Day 4 (OOP in Python):

Introduction to Object-Oriented Programming
Classes and Objects: The building blocks
Attributes & Methods (Instance, Class & Static)
__init__ Constructor and __str__ method
Encapsulation: Protecting data with access modifiers
Inheritance: Code reusability and hierarchy
Polymorphism: Method overriding and flexibility
Abstraction: Hiding complex implementation
Practical Data Science Examples:
Creating Custom Data Classes
Building a Simple Data Preprocessor Class
Model Classes for Machine Learning
Real project-style implementation
🛠️ Continuing to explore the Data Science ecosystem: Excel, Databases, Power BI, Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn & more.
📝 Today’s Quote:
“Think in Objects, Code like a Pro — OOP turns complexity into clarity!”
👉 We are now moving from procedural coding to professional-level programming. The foundation is getting stronger every day!
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Видео 4. OOPs in Data Science Training – Day 3 ( Nepali Explanation) канала Dhami Bytes