Duration: Instructor-led 4-days 

Course Description: 

This advanced course is designed for Python developers who are ready to deepen their expertise and write more efficient, maintainable, and professional-grade code. The course covers advanced language features, design patterns, concurrency, testing, metaprogramming, performance optimization, and system integration. You’ll also explore how Python fits into real-world production environments through hands-on labs and practical applications. 

By the end of this course, you’ll be able to build faster, more scalable applications and write Python code that’s production-ready and easy to maintain.

Prerequisites: 

• Solid understanding of Python syntax and core programming constructs 

• Experience working with Python data types, functions, and modules 

• Familiarity with object-oriented programming and basic file I/O 

• Exposure to libraries like requests, pandas, or flask is helpful but not required 

Key Takeaways: 

• Write clean, maintainable Python code using advanced language features 

• Understand Python internals and optimize code performance 

• Implement concurrency with threading, multiprocessing, and asyncio 

• Build, test, and package Python applications and libraries 

• Apply design patterns and best practices in real-world scenarios 

Module 1: Advanced Language Features & OOP 

Topics: 

• Review of Python internals and memory model 

• Advanced OOP: multiple inheritance, MRO, super() 

• Dunder methods and custom classes 

• Property decorators and class methods 

• Context managers and the with statement 

• Type annotations and dataclasses 

Hands-on Labs: 

• Create classes using __new__, __init__, and other magic methods 

• Build a custom context manager 

• Use dataclasses and typing to enforce better structure 

• Refactor existing OOP code for clarity and performance 

Module 2: Iterators, Generators, Functional & Metaprogramming 

Topics: 

• Iterators and generators in-depth 

• Generator pipelines and coroutines 

• Functional programming in Python: map, filter, reduce, functools 

• Closures, decorators, and higher-order functions 

• Introspection and metaprogramming with getattr, setattr, and type 

• Creating custom decorators and class decorators 

Hands-on Labs: 

• Build a generator-based data processing pipeline 

• Create and use custom decorators 

• Use introspection to dynamically access object attributes 

• Write a simple metaclass or class decorator for logging 

Module 3: Concurrency, Testing, and Package Development 

Topics: 

• Threading vs multiprocessing 

• Using concurrent.futures and asyncio 

• Asynchronous I/O and event loops 

• Writing testable code 

• Unit testing with unittest and pytest 

• Creating and publishing packages (setuptools, pyproject.toml) 

• Virtual environments and dependency management 

Hands-on Labs: 

• Write async code using async/await 

• Run CPU-bound vs I/O-bound tasks concurrently 

• Write unit tests and run them with pytest 

• Build and publish a simple Python package 

Module 4: Performance Tuning, System Integration & Real-World Applications 

Topics: 

• Profiling with cProfile, line_profiler, timeit 

• Code optimization techniques 

• Using NumPy, Cython, or Rust with Python for performance 

• File handling, logging, and configuration 

• Interfacing with OS and shell (os, subprocess, pathlib) 

• REST APIs with FastAPI or Flask 

• Packaging and deployment best practices 

Hands-on Labs: 

• Profile and optimize a slow Python script 

• Build and deploy a REST API using FastAPI 

• Use subprocess to automate CLI tools 

• Set up and use structured logging 

• Containerize a Python app using Docker (optional bonus)