🧪 Python Advanced Track
Advance Python Programming Course
Go beyond the basics — master decorators, generators, async programming,
multithreading, design patterns, testing strategies, packaging and performance
optimisation for production-grade Python applications.
Tools & Topics Covered
🐍 Python 3.12+
🔧 Decorators & Metaclasses
⚡ Generators & Coroutines
🧵 asyncio / aiohttp
🔄 Threading & Multiprocessing
🏗️ Design Patterns
🧪 pytest
📦 Packaging & PyPI
📊 NumPy / Pandas (intro)
📝 Type Hints & mypy
🐳 Docker for Python
🔧 Profiling & Optimisation
Course Summary
| Course Name |
Advance Python Programming |
| Duration |
40 Hours (Online / Classroom) |
| Prerequisites |
Basic Python — variables, OOP, file I/O, exception handling |
| Target Audience |
Python developers wanting to write production-grade, scalable code |
| Certificate |
Yes — Industry-recognised certificate upon completion |
| Mode |
Online & Classroom — with recorded sessions for lifetime access |
📋 Detailed Course Syllabus
🟣 Advanced Language Features
- First-class functions and closures — capturing variables from enclosing scope
- Writing and applying function decorators with
functools.wraps
- Decorator factories (decorators with arguments)
- Class decorators — using a class as a decorator
- Stacking multiple decorators and execution order
- Real-world use cases: logging, timing, authentication, caching
- Built-in decorators:
@property, @staticmethod, @classmethod
- Iterator protocol —
__iter__ / __next__ in depth
- Generator functions using
yield and yield from
- Generator expressions vs list comprehensions — memory trade-offs
- Sending values to generators with
send(); closing with close()
- Infinite sequences and lazy pipelines
- Context managers with
__enter__ / __exit__
contextlib.contextmanager decorator for clean context managers
- Python data model — how dunder methods power Python objects
- Custom dunder methods:
__getattr__, __setattr__, __delattr__, __slots__
- Descriptors —
__get__, __set__, __delete__ protocol
- Metaclasses —
type as a metaclass, custom __new__, __init_subclass__
- Abstract Base Classes (
abc.ABC, @abstractmethod)
- Dataclasses (
@dataclass) — auto-generating boilerplate
- Type hints —
typing module, generics, Protocol, TypeVar
🟡 Concurrency & Parallelism
- GIL (Global Interpreter Lock) — what it is and when it matters
threading module — Thread, Lock, RLock, Semaphore, Event- Thread-safe data structures —
queue.Queue
multiprocessing module — Process, Pool, shared memory, Managerconcurrent.futures — ThreadPoolExecutor and ProcessPoolExecutor- Choosing between threads and processes — CPU-bound vs I/O-bound
- Deadlocks, race conditions, and how to prevent them
- Event loop, coroutines, and
async def / await
asyncio.gather(), asyncio.create_task(), asyncio.wait()- Async context managers and async iterators
- Async I/O with
aiofiles and aiohttp
- Comparing sync vs async performance on I/O-bound workloads
- Running blocking code in async context using
loop.run_in_executor
- Async queues and producer-consumer patterns
🟢 Design Patterns & Architecture
- Creational: Singleton, Factory, Abstract Factory, Builder, Prototype
- Structural: Adapter, Decorator, Facade, Proxy, Composite
- Behavioural: Observer, Strategy, Command, Iterator, Template Method
- Pythonic implementations — using language features instead of boilerplate
- Dependency Injection in Python — manual and with frameworks
- Repository and Service layer patterns
🟠 Testing, Packaging & Performance
- pytest fundamentals — test discovery, fixtures, parametrize
- Mocking with
unittest.mock — MagicMock, patch, side_effect
- Test isolation and dependency injection in tests
- Code coverage with
pytest-cov
- Property-based testing with
hypothesis
- Integration tests and test databases
- CI integration — running tests in GitHub Actions
- Project structure best practices —
src/ layout
pyproject.toml, setup.cfg — modern packaging with setuptools / poetry- Building and publishing to PyPI with
twine
- Virtual environments —
venv, pipenv, poetry
- Semantic versioning and changelogs
- CLI tools with
argparse and click
- Dockerising a Python application —
Dockerfile, multi-stage builds
- Profiling with
cProfile, line_profiler, and memory_profiler
- Time complexity and Big-O — choosing the right data structure
functools.lru_cache and functools.cache for memoisation- NumPy for vectorised numerical computation
- Cython and Numba — introduction to compiled Python extensions
- Efficient string handling and I/O buffering
- Slots vs dicts for memory-efficient classes
- Python
logging module — handlers, formatters, levels, log rotation
- Structured logging with
structlog
- Configuration management —
.env, python-dotenv, pydantic-settings
- Secrets management — never hardcode credentials
- Security best practices — input validation, safe deserialization, SQL injection
- Dependency auditing with
pip-audit and safety
📦 Project Options:
- Task Queue System — asyncio, multiprocessing, Redis
- CLI Data Pipeline — CSV ingestion, validation, reporting
- Plugin Framework — metaclasses, dynamic loading, packaging
✅ Must-Include:
- Async or concurrent processing
- Design pattern(s) applied
- pytest with >80% coverage
- Packaged and Dockerised
- Logging and configuration management
