Mastering Object-Oriented Programming (OOPs) in Python

In this session, we explore the fundamentals of Object-Oriented Programming (OOPs) and why it is considered one of the most important topics in programming. You will first understand that data is the key to every application—whether it’s Facebook, Instagram, or Amazon—and that without proper data management, even the simplest program loses its meaning.

We then move into the challenges of handling data with only predefined structures like arrays and lists, and why there arises a need for custom data structures. This naturally introduces the concept of OOPs as a methodology to organize and manage data in a way that matches real-world requirements.

Key topics covered include:

• Why data is central to every app or program.
  

• The role of data structures in organizing information.
  

• Built-in structures (arrays, lists) vs. the need for custom data handling.
  

• Introduction to OOPs as a tool for creating custom data structures.
  

• Universality of OOPs across programming languages (C++, Java, Python, JavaScript, etc.).
  

By the end of the session, you will clearly understand why OOPs exists, the problems it solves, and how it becomes the foundation for structuring applications effectively across multiple domains.

In this session, we dive into a story-driven explanation of OOPs to make the concepts simple and relatable. Through the example of a company reception system, you will learn how unstructured data collection (visitors filling details on loose paper) leads to inconsistency and unusable information.

The session then shows how a structured approach (forms with predefined fields) ensures data consistency, reliability, and usefulness for future processes like sales or marketing. This analogy is mapped to the idea of classes and objects in OOPs, where a class acts as the “form design” and objects act as the “filled forms.”

Key topics covered include:

• How real-world problems map to OOPs concepts.

• The reception and visitor form analogy for classes and objects.

• Importance of structured data over random/unstructured input.

• Introduction to fields (variables/properties) within a class.

• Understanding custom data structures through real-life examples.

By the end of the session, you will understand the purpose of classes and objects and how OOPs helps enforce structure, uniformity, and efficiency in data handling.

In this session, we move from concept to implementation in Python. You will learn how a class works as a template (soft copy) and how creating an object is like generating a hard copy for real use.

We also explore how objects are stored in memory (RAM), how variables store memory addresses, and how multiple values can exist inside a single object. The session demonstrates how attributes (like name, phone, and inquiry) can be accessed, updated, and managed via objects.

Key topics covered include:

• Defining a class as a template or blueprint.

• Creating objects (instantiation) from a class.

• How objects are stored in memory with unique addresses.

• Difference between primitive variables and objects.

• Accessing and updating data using object properties.

By the end of the session, you will be able to relate OOPs theory with practical Python code, and clearly see how objects manage multiple values in an organized way.

In this session, we explore the fundamental idea of how classes act as blueprints and how objects serve as real-world instances that store data. You will first understand that primitive data structures like lists and arrays store individual pieces of information separately in memory, but objects allow us to group related information together in one block for faster retrieval and better organization.

Key topics covered include:

• The difference between independent variables and objects

• How classes provide a custom data structure

• Why OOP is required when managing multiple attributes of a single entity

• Real-world examples (e-commerce products, visitor forms)

• Grouping related attributes (name, phone, inquiry) inside an object

By the end of the session, you will clearly understand how a class defines the plan for storing data, how an object represents an actual instance with values, and why OOP makes programs more efficient and structured compared to using scattered variables.

In this session, we explore how to attach methods (functions inside a class) to operate on data and how Python automatically passes the object’s memory address to these methods using the self parameter.

Key topics covered include:

• Understanding self as a reference to the current object (not a keyword)

• Getter methods for reading object data

• Setter methods for updating/creating data

• Deleting attributes with the del keyword
  
  

• CRUD operations (Create, Read, Update, Delete) in OOPs

• Real examples: greeting users, updating phone numbers, deleting details

By the end of the session, you will clearly understand how methods connect to objects, how self provides access to object data, and how CRUD operations can be implemented securely with getter/setter methods.

In this session, we go deeper into the mechanics of classes, objects, and object-oriented design (OOD). You will learn how classes define the design/plan, while objects hold actual data in memory.

We also introduce the idea of encapsulation—why giving direct access to object variables is not always safe—and how it connects to access modifiers (public, private, protected).

Key topics covered include:

• Object-Oriented Design (OOD) as part of System Design.

• The role of classes in planning and structuring data.

• Objects as instances that store real information.

• Encapsulation basics—why direct access to attributes is unsafe.

• Introduction to access modifiers.

By the end of the session, you will understand the difference between designing with classes and implementing with objects, and why OOD is a fundamental skill in software engineering.

This session introduces the concept of encapsulation in depth using getter and setter methods. Instead of allowing direct modification of object data, functions (methods) are used to safely control how data is read or updated.

We discuss why uncontrolled access leads to issues and how encapsulation provides control, validation, and security.

Key topics covered include:

• Why direct access to object properties can be dangerous.

• Making properties private and using methods for controlled access.

• Getter and Setter functions as the interface between outside code and object data.

• Benefits of encapsulation: control, validation, and data security.

• CRUD operations (Create, Read, Update, Delete) in OOPs.

By the end of the session, you will understand how encapsulation enforces controlled data handling and ensures that objects expose only the necessary operations.

In this session, we explore the concept of the constructor (__init__ method in Python), which is automatically called when an object is created.

You will learn how constructors help initialize object attributes with default or custom values, and how they enforce consistent setup for every object.

Key topics covered include:

• Introduction to special methods in Python classes.

• The __init__ method as a constructor.

• Automatic execution of constructors during object creation.

• Difference between normal methods and constructors.

• Real-world analogy of constructing a “ready-to-use form.”

By the end of the session, you will understand how constructors streamline object initialization and why they are critical for building reliable applications.

In this session, we discuss the scope of variables within functions and classes. Using the concepts of local and global variables, we understand how data behaves inside methods versus across the entire program.

We extend this to classes and objects, learning how class-level data members act as global (shared) variables, while object-level attributes act as local (instance-specific) variables.

Key topics covered include:

• Local variables inside functions vs global variables.

• Lifetime of variables (stack memory vs persistent memory).

• Using the global keyword in Python.

• Difference between class attributes and instance attributes.

• How scope impacts object data management.

By the end of the session, you will understand how variable scope works in OOPs and how Python manages shared vs individual object data.

In this session, we explore the difference between class variables and instance variables in detail. You will learn how class variables are shared across all objects, while instance variables are unique to each object.

Through practical examples, we see how Python saves memory by referencing shared class variables, and how instance-specific updates override the shared defaults.

Key topics covered include:

• Class-level variables (shared/global across objects).

• Instance-level variables (unique/local to each object).

• Memory management and optimization in Python.

• How objects reference class variables by default.

• Overriding class variables with instance-specific values.

By the end of the session, you will understand how data is shared vs customized across objects, and how to effectively use class and instance variables in your designs.

In this session, we explore how constructors can be used for initializing objects and managing class-level variables. You will learn how constructors not only automate setup but also help track object creation and manage global data across multiple instances.

We also dive into the difference between local variables, object-level attributes, and class-level (global) variables. The session demonstrates how class data members can be modified using the class name, ensuring permanent information storage across objects.

Key topics covered include:

• Using constructors (__init__) to automate object initialization

• Tracking the number of created objects with class variables

• Local vs. global variables inside methods

• Making information permanent with class data members

• Passing values to constructors to reduce repetitive setter calls

• Default arguments in constructors for optional initialization

• Combining story-based examples with Python code for clarity

By the end of the session, you will clearly understand how constructors streamline data initialization, how class variables help retain shared information, and why constructors are powerful tools for building scalable, efficient OOP systems.

In this session, we dive into the concept of inheritance in object-oriented programming. You will learn how classes can reuse functionality from parent classes to avoid duplication.

Key topics covered include:

• Real-world analogy of parents and children inheriting traits

• Purpose of inheritance in programming

• Reusing parent class attributes and methods

• Behavior of constructors in inheritance

• Example: Visitor Form → Job Form → Course Form

By the end of the session, you will clearly understand the fundamentals of inheritance, why it is important, and how it simplifies code reusability and organization.

In this session, we extend the Visitor Form analogy into Job Form and Course Form to explain how inheritance supports data reusability and better user experience.

Key topics covered include:

• Single inheritance (one parent → one child)

• Multiple inheritance (child from multiple parents)

• Hierarchical inheritance (one parent → multiple children)

• Multi-level inheritance (child becoming parent for further children)

• Real-world mapping of inheritance types

By the end of the session, you will have a clear understanding of different inheritance types and when to apply them in data modeling.

In this session, we move to advanced concepts of inheritance with a focus on technical implementation in Python.

Key topics covered include:

• Method overriding in child classes

• Method Resolution Order (MRO) in Python

• Hybrid inheritance and its use cases

• Practical examples of extending functionality without duplication

By the end of the session, you will understand how advanced inheritance concepts enable extensibility, maintainability, and modular design.

In this session, we explore how Python resolves method calls in multi-level and multiple inheritance scenarios.

Key topics covered include:

• Method overriding and conflicts in inheritance

• How Python searches methods using MRO (left-to-right resolution)

• Using super() to call parent constructors and methods

• Real-world examples of form-handling with inheritance

By the end of the session, you will clearly understand how Python resolves conflicts in multiple inheritance, how to use super() effectively, and how to design maintainable OOP solutions.

In this session, we explore the fundamentals of file handling in Python and why it is essential for making data storage permanent. You will first understand the limitation of storing data only in RAM (volatile memory) and why we require files on storage devices like hard disks, pen drives, or cloud storage for persistence.

We then discuss how files act as variables that store memory addresses of data on the hard disk, enabling us to retrieve and manage information across multiple program executions. The session also introduces the basic file operations and the idea of different file modes.

Key topics covered include:

• Why RAM is volatile and cannot store data permanently

• Files as containers (or handlers) for permanent data storage

• Difference between variables in RAM vs. files on storage devices

• File as a pointer to a physical memory address on disk

• Basic file operations: create, read, write, delete, append

• Introduction to file modes (read, write, append)

By the end of the session, you will understand why file handling is necessary, how files act as handlers for disk storage, and how basic operations on files enable persistent data storage.

In this session, we explore how Python interacts with files through file handlers and how reading operations are managed internally. You will first learn how to open a file using Python’s open() function, which creates a file handler pointing to the file’s address on the hard disk.

We then move into reading data character by character, reading specific chunks, and understanding how Python maintains a cursor (iterator) while traversing file contents. Special functions like tell() and seek() are introduced to track and move the file cursor.

Key topics covered include:

• Opening files with the open() function and file handlers

• Concept of IO (Input/Output) operations with files

• Using read() to read one or multiple characters

• End-of-line (\n, \r\n) and end-of-file markers in files

• The role of the cursor in reading sequentially

• Using tell() to find the cursor’s position

• Using seek() to reposition the cursor anywhere in the file

• Closing files with close() to release resources

By the end of the session, you will understand how Python reads data from files, how the cursor works in file reading, and how to use file handlers effectively for controlled access.

In this session, we explore how to write and append data to files in Python using different modes. You will first learn how write() stores data temporarily in RAM before committing it to disk upon closing the file.

We then discuss the difference between write (w), append (a), read (r) modes, and combined modes like r+ and w+. Practical demonstrations show how overwriting, appending, and simultaneous read-write operations work. The concept of text vs. binary file handling is also introduced.

Key topics covered include:

• Writing data with write() and when changes are saved permanently

• Difference between write mode (w), read mode (r), and append mode (a)

• How append mode preserves old data and adds new data at the end

• Combined modes:

  • r+ → read and write (file must exist)

  • w+ → write and read (creates new file if not existing, overwrites if it exists)

• Role of the cursor during writing (seek and overwrite at specific positions)

• Difference between text files (human-readable) and binary files (PDFs, images, videos)

• Introduction to bytes data type for handling binary files

By the end of the session, you will be confident in creating, writing, appending, and managing file data in different modes, while also understanding the difference between text and binary file handling in Python.