Introduction

This roadmap is designed to take you from zero to proficient in Python OOP, with hands-on projects, real-world examples, and deep dives into advanced patterns. By the end, you’ll write clean, scalable, and Pythonic OOP code.

 

Roadmap Outline

Module 1: Introduction to Python Object-Oriented Programming

📌 Goal: Understand why OOP is used and how Python implements it.

• What is Object-Oriented Programming (OOP)?
• Procedural vs. Object-Oriented vs. Functional Programming
• Key OOP Concepts: Encapsulation, Inheritance, Polymorphism, Abstraction
• Why Use OOP in Python?
• Python’s OOP Implementation vs. Java/C++
• Setting Up Your Python Environment for OOP Development

Module 2: Classes, Objects, and Attributes

📌 Goal: Learn how to create and work with classes and objects.

• Defining a Class (class keyword)
• Creating and Instantiating Objects
• Class Attributes vs. Instance Attributes
• The self Keyword – Why is it Required?
• Modifying Object Attributes Dynamically
• Class Variables and Instance Variables
• Built-in __dict__ Attribute
• Memory Allocation of Objects in Python

Module 3: Methods and Encapsulation

📌 Goal: Learn about instance methods, class methods, and encapsulation principles.

• Instance Methods vs. Class Methods vs. Static Methods
• Encapsulation: Public, Protected, and Private Members
• Getters and Setters (@property Decorator)
• The @classmethod and @staticmethod Decorators
• Modifying Class and Instance Data Securely
• Using __slots__ to Optimize Memory Usage

Module 4: Inheritance and Polymorphism

📌 Goal: Understand inheritance, method overriding, and multiple inheritance.

• Single Inheritance
• Method Overriding
• The super() Function and MRO (Method Resolution Order)
• Multiple Inheritance and the Diamond Problem
• Polymorphism and Duck Typing (isinstance, hasattr)
• Using @final Decorator to Prevent Inheritance (Python 3.8+)

Module 5: Advanced OOP Features

📌 Goal: Understand how to manage classes efficiently.

• Composition vs. Inheritance
• Class Factories and Object Factories
• Using __new__() for Advanced Object Creation
• Deep Copy vs. Shallow Copy (copy module)
• Singleton Pattern Implementation in Python

Module 6: Abstract Classes, Interfaces, and Mixins

📌 Goal: Learn how to implement abstract base classes (ABCs) and interfaces.

• Why Use Abstract Classes?
• Creating Abstract Base Classes (ABC Module)
• Interfaces in Python (Using ABCs)
• Mixin Classes – Extending Functionality Without Inheritance

Module 7: Metaprogramming & Metaclasses

📌 Goal: Learn about metaclasses and how Python modifies classes dynamically.

• What is a Metaclass?
• Custom Metaclasses (type and __new__)
• Modifying Class Behavior at Runtime
• Enforcing Coding Standards Using Metaclasses
• Using __call__() for Callable Classes

Module 8: Special (Dunder) Methods in Python

📌 Goal: Understand how Python classes interact with built-in methods.

• Magic Methods (__init__, __str__, __repr__, __del__)
• Operator Overloading (__add__, __eq__, __lt__, etc.)
• Context Managers (__enter__, __exit__)
• Custom Iterators (__iter__, __next__)

Module 9: Design Patterns in Python

📌 Goal: Implement reusable and scalable OOP design patterns.

• Creational Patterns

  • Singleton
    

    Ensuring Only One Instance Exists.

  • Factory

    Creating Objects Based on Input.

• Structural Patterns

  • Adapter

    Making Incompatible Interfaces Work Together.

  • Decorator

    Enhance objects by adding new functionalities without modifying their structure.

  • Composite

    Enable developer to compose objects into tree structure and work these structure as individual objects.

• Behavioral Patterns

  • Observer

    Event-Driven Systems (e.g., Notifications)

  • Strategy

    This pattern turns a set of behaviors into objects and makes them interchangeable inside original context object.

Module 10: Performance Optimization in OOP

📌 Goal: Improve the efficiency of Python OOP programs.

• Using __slots__ to Reduce Memory Overhead
• Profiling Python Classes (cProfile, line_profiler)
• Optimizing Performance with functools.lru_cache

Module 11: Real-World OOP Project in Python

📌 Goal: Apply OOP knowledge to build a real-world application.

• OOP in Web Development
  • Django models, Flask Class-Based Views.
• OOP in Data Science
  • Custom Scikit-learn Estimators
• OOP in APIs
  • Building RESTful APIs with Class-Based Views ( FastAPI/DRF)
• Project: Developing an E-commerce System Using OOP
  • Implementing Models (User, Product, Cart, Order)
  • Applying Design Patterns in a Real Application

Roadmap Outcome

By the end of this course, you will:

✅ Master Python’s OOP principles from basics to advanced.

✅ Avoid common pitfalls like tight coupling.

✅ Write clean, maintainable, and reusable OOP-based Python code.

✅ Write clean, maintainable, and reusable OOP-based Python code.

✅ Implement advanced OOP techniques like metaprogramming, abstract classes, and design patterns