Design Patterns with Python: A Practical Guide

Design Patterns with Python: A Practical Guide

Master GoF patterns with idiomatic Python: factory, strategy, observer, adapter, from theory to production code.

What you’ll learn
⚡ Learn the concepts of Design Patterns and how to apply them
⚡ Develop more concise and cohesive software with the correct application of patterns
⚡ Understand how to construct the main design patterns in Python
⚡ Understand how to integrate different design patterns with Python
⚡ Evaluate real situations where design patterns can be applied

Requirements
❗ Intermediate knowledge in the Python language
❗ Basic knowledge in software development lifecycle
❗ Basic knowledge in business logic
❗ Basic knowledge in Algorithms and Data Structures

DescriptionDesign patterns are the shared vocabulary of professional software engineering. Every senior developer is expected to know them, every code review references them, and every system that survives past its first year uses them — whether the team named them or not. This course teaches the most important Gang of Four patterns through idiomatic Python, not through Java-era boilerplate.

Every pattern in the course follows the same structure: the problem, the anti-pattern, the refactored solution, and a test proving correctness. Code examples use typing.Protocol for structural subtyping, dataclasses for clean data holders, and pytest for verification — the same stack professional Python teams use today.

You will implementfour creational patterns (Factory, Abstract Factory, Prototype, Builder),five structural patterns (Adapter, Bridge, Flyweight, Proxy, Facade), andsix behavioral patterns (Command, Strategy, State, Observer, Template Method, Iterator). Each has a bad-to-good code transformation you can run locally.

The course opens with business modeling fundamentals and BPMN notation — because patterns emerge from business processes, not from memorizing UML. Section 2 includes an AI-assisted mapping lecture where you use an LLM to propose patterns for a described workflow, then validate and refine the suggestions. The final section covers validation with AI, promotion to production, and the YAGNI/SOLID balance that keeps patterns useful rather than ceremonial.

Enroll now and build the pattern vocabulary that makes your code reviews sharper, your architectures cleaner, and your refactoring stories shorter.

Who this course is for
⭐ Basic and composed data structures of Python
⭐ Intermediate understanding on Algorithms and Data Structures
⭐ Basic understanding of design patterns

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