This module teaches learners how to read from and write to files in Python, covering essential techniques like file opening, reading line by line, writing with different modes, and appending data. It also includes best practices for managing file operations effectively.

This module explores advanced Python concepts including higher-order functions, nested functions, variable scope, and decorators. Learners will gain a deep understanding of how to manipulate functions, manage variable lifecycles, and dynamically extend function behavior using decorators. The content includes practical examples and coding exercises to reinforce key principles.

This module introduces the fundamental concepts of object-oriented programming in Python, focusing on how to define and use classes, create objects, and manage attributes and methods. Learners will gain hands-on experience with class instantiation, the __init__ method, and setting default attribute values. By the end, students will be able to structure their code using OOP principles effectively.

This module explores advanced concepts in object-oriented programming in Python, including instance methods, class methods, static methods, encapsulation, and attribute management. Learners will gain hands-on experience with defining and using properties, understanding attribute lookup order, and dynamically managing object attributes. The module emphasizes practical implementation through coding exercises and solutions.

This module explores Python's magic methods, teaching learners how to customize object behavior for operations like equality, string representation, and iteration. It covers practical implementation of methods such as __str__, __eq__, __len__, and __getitem__, and includes coding exercises to reinforce understanding. Learners will also gain insights into docstrings and the namedtuple object for better code documentation and structure.

This module explores the fundamentals of inheritance in Python, including subclassing, method overriding, and the use of the super() function. It also covers advanced topics like polymorphism, multiple inheritance, and composition as alternatives to inheritance. Learners will gain practical skills in designing and implementing object-oriented code structures.

This module provides a comprehensive guide to handling errors in Python through exception handling techniques. Learners will explore how to use try-except blocks, raise exceptions, and create custom error classes. The module also covers best practices for structured error management using else and finally blocks.

This module covers the fundamentals of working with dates and time in Python, including creating, manipulating, and formatting date, time, and datetime objects. Learners will gain hands-on experience with the datetime module and learn to perform time-based calculations and comparisons.

This module covers the essential functions in Python's random module, such as generating random numbers, selecting elements from sequences, and shuffling lists. Learners will understand how to implement randomness effectively in their code and recognize the proper use cases for each function.

This module introduces learners to the fundamentals of unit testing in Python, covering essential tools like the assert statement, doctest, and unittest modules. It explores how to write and organize tests, handle errors, and ensure code reliability through various assertion methods and test lifecycle functions.

This module teaches how to use Python's unittest.mock module to create and manage mock objects, enabling effective unit testing by simulating external dependencies. Learners will explore attributes like return_value and side_effect, as well as tools like patch() and @patch decorator to isolate code components during testing. By the end, students will be able to build reliable and maintainable test cases using advanced mocking techniques.