This module introduces the programming constructs used to control program flow and organize ordered data. Learners use conditional statements to support decision-making and loops to repeat actions and process data. They also trace code execution and identify common control-flow errors. The module then introduces Python lists, including their creation, modification, indexing, and slicing, enabling learners to store and process related values in simple programs.

This module develops learners’ ability to organize Python code into reusable components. Learners create and call functions, work with parameters and return values, manage variable scope, and apply function-writing best practices. They also decompose programming problems into smaller tasks and create simple classes with attributes and methods. The module concludes with built-in modules, custom modules, external libraries, and the use of the pip package manager to extend Python’s functionality.

This module examines Python data structures used to organize, access, and manipulate information. Learners compare lists, tuples, dictionaries, and sets according to characteristics such as ordering, mutability, uniqueness, and access patterns. They apply these structures to practical data-management tasks and select suitable structures for different programming requirements. The module also introduces list sorting and linear and binary searching techniques for organizing and locating data.

This module develops the skills required to identify and resolve errors in Python programs. Learners examine common exceptions, interpret error messages, and use print statements and interactive debugging tools to trace program execution. They also use try-except blocks to manage anticipated exceptions and prevent unexpected program termination. A systematic debugging process is applied to locate causes, test corrections, and improve program reliability.

This module introduces practices used to test, manage, and present Python projects. Learners examine the purpose of unit testing and use pytest to create, organize, and execute tests. They then use Git to track changes and manage branches and distinguish Git-based version control from GitHub-based project hosting. The module concludes with the development of a professional GitHub portfolio that presents Python projects and technical skills.