This module introduces learners to the transformative role of Generative AI (GenAI) in modern software development, specifically focusing on Python. Learners will explore how GenAI tools can be leveraged to automate various aspects of the development lifecycle, including code generation, review, optimization, testing, and documentation. The module emphasizes practical applications of GenAI, providing hands-on experience with popular tools and techniques for effective prompt engineering. Learners will discover how to craft precise prompts to generate code, identify bugs and vulnerabilities, refactor code for improved readability, and create comprehensive test cases and documentation. While highlighting the potential of GenAI to enhance productivity and efficiency, the module also addresses its limitations and emphasizes the crucial role of human oversight in ensuring code quality and ethical considerations.

This module provides learners with a comprehensive introduction to cloud computing and its significance for Python developers. It begins with an overview of core cloud concepts, including service models (IaaS, PaaS, SaaS), and explores leading cloud platforms like Microsoft Azure. Learners will gain practical experience with Azure, creating accounts, navigating the Azure portal, and deploying a simple application. The module then delves into various deployment strategies, covering virtual machines, containers, and serverless functions. Learners will deploy a Flask web application to Azure and explore serverless computing with Azure Functions. Finally, the module showcases the breadth of cloud services available to Python developers, including storage, databases, and machine learning. Through hands-on demonstrations and activities, learners will gain practical experience interacting with Azure services using the Python SDK and build a serverless image processing application.

This module emphasizes the critical importance of documentation in professional Python development. Learners explore the purpose and value of documentation in creating maintainable, collaborative codebases. They delve into best practices for writing effective documentation, adhering to PEP 8 style guidelines, and using tools like Sphinx to generate professional-grade documentation from their code. The module then introduces the role of GenAI in automating documentation tasks, including generating code comments and API documentation. Learners will practice prompt engineering techniques to refine GenAI outputs and ensure accuracy and completeness. The module further covers principles of clean code, SOLID design principles, and refactoring techniques to improve code readability and maintainability. Finally, it introduces asynchronous programming in Python using the asyncio library, enabling learners to write concurrent code for handling I/O-bound operations efficiently.

This module shifts focus from testing individual components (unit testing) to verifying the interactions between different parts of a software system. Learners explore the concept of integration testing and its importance in identifying defects that may arise when individual units are combined. They learn about various integration testing strategies, including top-down, bottom-up, and sandwich approaches, and understand their strengths and weaknesses. The module provides hands-on experience with mocking dependencies using libraries like pytest-mock to isolate components and simulate external interactions. Learners also delve into the concept of test doubles (mocks, stubs, fakes) and learn how to choose the appropriate type for specific testing scenarios. The module culminates with a practical demonstration of integration testing a Flask web application using the pytest framework. Additionally, learners are introduced to Test-Driven Development (TDD) as a development methodology that promotes writing tests before code, leading to improved code quality and developer confidence.