Course Objectives

• Write efficient, maintainable, and scalable Python code

• Apply advanced language features and design patterns

• Build modular applications and APIs

• Optimize performance and manage concurrency

• Test, debug, and deploy Python applications

Prerequisites

• Strong knowledge of basic Python

• Familiarity with functions, classes, and file handling

• Basic understanding of data structures

Module 1: Advanced Python Fundamentals

• Python internals and execution model

• Memory management and garbage collection

• Mutable vs immutable objects

• PEP 8 and best practices

• Lab: Memory inspection and code refactoring

Module 2: Advanced Data Structures

• Collections module

• Custom data structures

• HeapQ and Bisect

• Lab: LRU cache and task scheduler

Module 3: Advanced OOP

• Magic methods

• Abstract base classes

• Multiple inheritance and MRO

• Dataclasses

• Lab: Plugin-based application

Module 4: Functional Programming

• Decorators and closures

• itertools and functools

• Lab: Custom decorators and pipelines

Module 5: Exception Handling & Logging

• Custom exceptions

• Context managers

• Advanced logging

• Lab: Centralized logging system

Module 6: File Handling & Serialization

• CSV, JSON, Pickle

• Large file handling

• Lab: File-based data processor

Module 7: Concurrency & Parallelism

• Multithreading and multiprocessing

• asyncio

• Lab: Async web scraper

Module 8: Networking & APIs

• REST APIs with FastAPI

• Authentication basics• Lab: Build and secure an API

Module 9: Database Programming

• SQLAlchemy ORM

• SQLite / PostgreSQL

• Lab: CRUD application

Module 10: Testing & Debugging

• pytest and unittest

• Mocking

• Lab: Test coverage improvement

Module 11: Performance Optimization

• Profiling

• Caching

• Lab: Optimize slow code

Module 12: Packaging & Deployment

• Virtual environments

• Docker

• CI/CD basics

• Lab: Dockerized Python app

Capstone Project

Develop a real-world Python application incorporating APIs, databases, concurrency, testing, and

deployment best practices.

Duration

60–80 Hours (Theory + Labs)