Executive Development Programme in Developing Python Packages: Best Practices and Tools

This course is designed for software engineers, data scientists, and IT professionals looking to enhance their Python package development skills. Participants will learn best practices for creating, testing, and deploying high-quality Python packages, as well as how to effectively use modern tools and frameworks.

By the end of the program, learners will gain proficiency in managing dependencies, automating builds, and optimizing package performance. They will also master the use of popular tools such as setuptools, pip, and pytest, ensuring they can deliver robust and maintainable Python software solutions.

1. 1. Introduction to Python Package Development: Learners will understand the basics of creating Python packages, including package structure and the importance of version control. They will gain practical skills in setting up a Python project and using version control systems like Git.
2. 2. Packaging Fundamentals: This module covers essential packaging concepts such as `setup.py` and `pyproject.toml`, and introduces learners to tools like `setuptools` and `wheel`. By the end, learners will be able to create a basic package and publish it to a package repository.
3. 3. Best Practices for Code Organization: Learners will study best practices for organizing code within a package, including design patterns and module structure. Practical skills gained include refactoring code for better maintainability and readability.
4. 4. Testing and Quality Assurance: This module focuses on writing and running tests for Python packages using frameworks like `pytest`. Learners will learn how to set up automated testing, coverages, and continuous integration pipelines to ensure code quality.
5. 5. Documentation and User Guides: Learners will learn how to write clear and concise documentation using tools like `Sphinx`. They will gain skills in creating user guides and API documentation to effectively communicate the functionality and usage of their packages.
6. 6. Packaging for Distribution: This module covers the process of preparing a package for distribution, including creating distributions, uploading them to package repositories, and managing dependencies. Practical skills include using `twine` and understanding package indexes like PyPI.
7. 7. Advanced Packaging Techniques: Learners will explore advanced packaging techniques such as dependency management, conditional builds, and custom build systems. They will gain skills in crafting sophisticated package configurations to support diverse deployment scenarios.
8. 8. Security and Dependency Management: This module focuses on security best practices in packaging, including managing dependencies and securing package repositories. Practical skills include using tools like `pip check`, `bandit`, and `PyUp` to identify and mitigate security risks.
9. 9. Performance Optimization: Learners will learn strategies for optimizing the performance of Python packages, including profiling and benchmarking. Practical skills include using profiling tools and techniques to improve the speed and efficiency of their code.
10. 10. Advanced Testing Strategies: This module delves into advanced testing strategies, including performance testing, stress testing, and integration testing. Learners will gain skills in setting up comprehensive testing environments to ensure robust package functionality.