lnet_tutor/learning_plans/python/00_PYTHON_MASTER_PLAN.md

Python Advanced Learning - Master Plan

🎯 Goal: Advanced Python Mastery

This comprehensive plan will guide you from fundamentals to advanced Python expertise, covering everything from basics to expert-level topics.

📊 Learning Journey Overview

Total Duration: 12-18 months (depending on pace) Target Level: Advanced/Expert Python Developer Daily Commitment: 2-3 hours recommended

🗺️ Learning Path Structure

Phase 1: Foundations (2-3 months)
    └─> Python Basics & Core Concepts
    
Phase 2: Intermediate (3-4 months)
    └─> Data Structures, OOP, Functional Programming
    
Phase 3: Advanced (4-5 months)
    └─> Metaprogramming, Concurrency, Performance
    
Phase 4: Expert (3-4 months)
    └─> Advanced Patterns, System Design, Best Practices
    
Phase 5: Specialization (Ongoing)
    └─> Choose your domain (Web, Data Science, DevOps, etc.)

📚 Learning Modules Breakdown

Phase 1: Python Foundations (Beginner to Intermediate)

Duration: 2-3 months | Difficulty: ⭐⭐☆☆☆

1. Module 1.1: Python Basics (2 weeks)

   • Installation & Environment Setup
   • Variables, Data Types, Operators
   • Input/Output, String Operations
   • Control Flow (if/elif/else)
   • Loops (for, while, break, continue)
   • Basic Error Handling

2. Module 1.2: Data Structures (2 weeks)

   • Lists, Tuples, Sets
   • Dictionaries
   • List Comprehensions
   • Dictionary & Set Comprehensions
   • Collections Module (namedtuple, defaultdict, Counter, deque)

3. Module 1.3: Functions & Modules (2 weeks)

   • Function Definition & Arguments
   • *args and **kwargs
   • Lambda Functions
   • Map, Filter, Reduce
   • Modules & Packages
   • Import System

4. Module 1.4: File Handling & Exception Handling (1 week)

   • Reading & Writing Files
   • Context Managers (with statement)
   • Exception Types
   • Try/Except/Finally
   • Custom Exceptions
   • Raising Exceptions

5. Module 1.5: Object-Oriented Programming Basics (3 weeks)

   • Classes & Objects
   • Attributes & Methods
   • init and str
   • Inheritance (Single & Multiple)
   • Encapsulation
   • Polymorphism
   • Method Overriding

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Phase 2: Intermediate Python (Intermediate)

Duration: 3-4 months | Difficulty: ⭐⭐⭐☆☆

6. Module 2.1: Advanced OOP (2 weeks)

   • Property Decorators (@property)
   • Class Methods & Static Methods
   • Abstract Base Classes (ABC)
   • Method Resolution Order (MRO)
   • Multiple Inheritance & Mixins
   • Composition vs Inheritance

7. Module 2.2: Iterators & Generators (2 weeks)

   • Iterator Protocol (iter, next)
   • Generator Functions (yield)
   • Generator Expressions
   • itertools Module
   • Memory Efficiency with Generators
   • Coroutines Basics

8. Module 2.3: Decorators (2 weeks)

   • Function Decorators
   • Class Decorators
   • Decorator Patterns
   • Functools Module (wraps, lru_cache, partial)
   • Chaining Decorators
   • Decorators with Arguments

9. Module 2.4: Context Managers (1 week)

   • enter and exit
   • contextlib Module
   • Custom Context Managers
   • contextmanager Decorator
   • Resource Management Patterns

10. Module 2.5: Regular Expressions (1 week)

    • Regex Syntax & Patterns
    • re Module Functions
    • Match Objects
    • Groups & Capturing
    • Lookahead & Lookbehind
    • Regex Performance

11. Module 2.6: Functional Programming (2 weeks)

    • First-Class Functions
    • Higher-Order Functions
    • Closures
    • Partial Application
    • Immutability Concepts
    • Functional Tools (map, filter, reduce)
    • operator Module

12. Module 2.7: Type Hints & Static Typing (2 weeks)

    • Type Annotations
    • typing Module (List, Dict, Optional, Union)
    • Generic Types
    • Protocol & TypedDict
    • mypy Static Type Checker
    • Type Checking Best Practices

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Phase 3: Advanced Python

Duration: 4-5 months | Difficulty: ⭐⭐⭐⭐☆

13. Module 3.1: Metaclasses (2 weeks)

    • What are Metaclasses?
    • type() Function
    • new vs init
    • Creating Custom Metaclasses
    • Metaclass Use Cases
    • Class Creation Process

14. Module 3.2: Descriptors (2 weeks)

    • Descriptor Protocol
    • get, set, delete
    • Data vs Non-Data Descriptors
    • Property Implementation
    • Descriptor Use Cases
    • Managed Attributes

15. Module 3.3: Magic Methods (Dunder Methods) (2 weeks)

    • Operator Overloading
    • repr vs str
    • call, getitem, setitem
    • len, contains
    • Comparison Methods (eq, lt, etc.)
    • Arithmetic Methods
    • Context Manager Methods

16. Module 3.4: Memory Management (2 weeks)

    • Python Memory Model
    • Reference Counting
    • Garbage Collection
    • gc Module
    • Memory Profiling
    • slots for Memory Optimization
    • Weak References

17. Module 3.5: Concurrency - Threading (2 weeks)

    • threading Module
    • Thread Objects
    • Locks & RLocks
    • Semaphores & Events
    • Thread-Safe Queues
    • GIL (Global Interpreter Lock)
    • Thread Pool Executor

18. Module 3.6: Concurrency - Multiprocessing (2 weeks)

    • multiprocessing Module
    • Process Objects
    • Inter-Process Communication (Pipes, Queues)
    • Shared Memory (Value, Array, Manager)
    • Process Pool Executor
    • When to Use vs Threading

19. Module 3.7: Asynchronous Programming (3 weeks)

    • asyncio Fundamentals
    • async/await Syntax
    • Event Loop
    • Coroutines & Tasks
    • Futures
    • asyncio Streams
    • aiohttp for Async HTTP
    • Async Context Managers & Iterators

20. Module 3.8: Performance Optimization (2 weeks)

    • Profiling (cProfile, line_profiler)
    • Benchmarking (timeit)
    • Performance Best Practices
    • Algorithm Optimization
    • Data Structure Selection
    • Caching Strategies
    • JIT Compilation (Numba, PyPy)

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Phase 4: Expert Python

Duration: 3-4 months | Difficulty: ⭐⭐⭐⭐⭐

21. Module 4.1: Advanced Design Patterns (3 weeks)

    • Creational Patterns (Singleton, Factory, Builder)
    • Structural Patterns (Adapter, Decorator, Facade)
    • Behavioral Patterns (Observer, Strategy, Command)
    • Python-Specific Patterns
    • Anti-Patterns to Avoid

22. Module 4.2: Testing & Quality Assurance (3 weeks)

    • unittest Framework
    • pytest Framework
    • Test Fixtures & Parametrization
    • Mocking & Patching
    • Coverage Analysis
    • Test-Driven Development (TDD)
    • Property-Based Testing (Hypothesis)

23. Module 4.3: Debugging & Profiling (2 weeks)

    • pdb Debugger
    • Remote Debugging
    • Memory Leak Detection
    • Performance Profiling
    • Logging Best Practices
    • Error Tracking

24. Module 4.4: Package Development (2 weeks)

    • Project Structure
    • setup.py & pyproject.toml
    • setuptools & pip
    • Virtual Environments (venv, virtualenv)
    • Dependency Management (pip, Poetry, pipenv)
    • Publishing to PyPI

25. Module 4.5: C Extensions & Cython (2 weeks)

    • Python C API
    • ctypes & cffi
    • Cython Basics
    • Performance with Cython
    • Interfacing with C Libraries
    • When to Use C Extensions

26. Module 4.6: Advanced Data Manipulation (2 weeks)

    • NumPy Fundamentals
    • Pandas Basics
    • Data Processing Patterns
    • Memory-Efficient Data Processing
    • Working with Large Datasets

27. Module 4.7: Network Programming (2 weeks)

    • socket Module
    • TCP/IP Programming
    • UDP Programming
    • HTTP Clients & Servers
    • WebSocket Programming
    • Network Security Basics

28. Module 4.8: Database Programming (2 weeks)

    • DB-API 2.0 Standard
    • SQLite, PostgreSQL, MySQL
    • Connection Pools
    • ORM Concepts (SQLAlchemy basics)
    • NoSQL with Python (MongoDB, Redis)
    • Database Best Practices

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Phase 5: Python Specializations (Choose Your Path)

Duration: Ongoing | Difficulty: ⭐⭐⭐⭐⭐

29. Specialization A: Web Development

    • Django Framework (Advanced)
    • Flask/FastAPI
    • REST API Design
    • GraphQL
    • WebSockets
    • Authentication & Authorization
    • Web Security
    • Deployment & Scaling

30. Specialization B: Data Science & ML

    • Advanced NumPy & Pandas
    • Data Visualization (Matplotlib, Seaborn)
    • Scikit-learn
    • TensorFlow/PyTorch Basics
    • Data Pipelines
    • Feature Engineering
    • Model Deployment

31. Specialization C: DevOps & Automation

    • System Administration with Python
    • Automation Scripts
    • CI/CD Pipelines
    • Docker & Kubernetes
    • Infrastructure as Code
    • Monitoring & Logging
    • Cloud Services (AWS, GCP, Azure)

32. Specialization D: Security & Cryptography

    • cryptography Module
    • Secure Coding Practices
    • Penetration Testing
    • Web Security
    • Authentication Systems
    • Encryption & Hashing

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📈 Progress Tracking

Mastery Levels

• Level 0: Unfamiliar - Never encountered
• Level 1: Aware - Know it exists, basic understanding
• Level 2: Competent - Can use with documentation
• Level 3: Proficient - Can use without documentation
• Level 4: Expert - Can teach others, optimize, debug complex issues

Weekly Goals

• Complete 1-2 modules per month
• Practice coding daily (30-60 minutes minimum)
• Build 1 small project per week
• Read Python source code weekly
• Contribute to open source monthly

Monthly Assessments

• Take comprehensive exam covering month's topics
• Build one medium-sized project
• Write blog post explaining learned concepts
• Review and refactor old code

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🎓 Learning Resources

Books (Recommended Reading Order)

1. "Python Crash Course" by Eric Matthes (Foundations)
2. "Fluent Python" by Luciano Ramalho (Intermediate-Advanced)
3. "Effective Python" by Brett Slatkin (Best Practices)
4. "Python Cookbook" by David Beazley (Advanced Techniques)
5. "High Performance Python" by Micha Gorelick (Optimization)

Online Resources

• Official Python Documentation (python.org)
• Real Python (realpython.com)
• Python Enhancement Proposals (PEPs)
• Python Package Index (PyPI)
• Stack Overflow Python Tag

Practice Platforms

• LeetCode (Algorithm practice)
• HackerRank (Python challenges)
• Codewars (Python kata)
• Project Euler (Math/Programming problems)
• Real-world projects on GitHub

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🏆 Milestones & Achievements

Milestone 1: Python Basics Complete (Month 2-3)

• ✅ Can write basic scripts
• ✅ Understand all basic data structures
• ✅ Can use functions and modules
• ✅ Handle files and exceptions
• 🎯 Project: Build a CLI todo app

Milestone 2: Intermediate Python (Month 5-7)

• ✅ Master OOP concepts
• ✅ Write decorators and generators
• ✅ Use type hints effectively
• ✅ Understand functional programming
• 🎯 Project: Build a web scraper with data storage

Milestone 3: Advanced Python (Month 9-12)

• ✅ Understand metaclasses and descriptors
• ✅ Master async programming
• ✅ Optimize code performance
• ✅ Write concurrent programs
• 🎯 Project: Build an async web server

Milestone 4: Expert Python (Month 13-16)

• ✅ Master design patterns
• ✅ Write comprehensive tests
• ✅ Create distributable packages
• ✅ Understand Python internals
• 🎯 Project: Publish an open-source package

Milestone 5: Specialization (Month 17-18+)

• ✅ Master chosen specialization
• ✅ Contribute to major projects
• ✅ Build production-ready applications
• 🎯 Project: Major portfolio project

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📝 Assessment Strategy

Weekly Quizzes

• 10-15 questions on week's topics
• Mix of single choice, multiple choice, true/false
• "I don't know" option available for honest assessment

Monthly Comprehensive Exams

• 30-50 questions covering all monthly topics
• Includes coding exercises
• Auto-graded with detailed feedback

Quarterly Projects

• Build substantial projects
• Code review and feedback
• Real-world application focus

Continuous Assessment

• Track time spent on each topic
• Monitor quiz/exam scores
• Identify weak areas for review
• Adjust learning pace as needed

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🚀 Getting Started

Week 1 Action Plan

1. Set up Python development environment
2. Install essential tools (IDE, Git)
3. Read "Python Basics" module
4. Complete 5 beginner exercises
5. Take first quiz on basics

Daily Study Routine

• Morning (30 min): Read theory/documentation
• Afternoon (60 min): Hands-on coding practice
• Evening (30 min): Review, quiz, or project work

Weekend Activities

• Build small projects
• Read Python source code
• Watch Python conference talks
• Contribute to open source

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💡 Learning Tips

1. Code Every Day: Even 30 minutes makes a difference
2. Read Others' Code: Learn from experienced developers
3. Build Projects: Apply what you learn immediately
4. Teach Others: Best way to solidify understanding
5. Review Regularly: Spaced repetition is key
6. Don't Rush: Deep understanding > speed
7. Ask Questions: Engage with Python community
8. Stay Updated: Follow Python news and PEPs

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🔗 Next Steps

1. Review the detailed modules in 01_KNOWLEDGE_GRAPH.md
2. Check your current level in 02_ASSESSMENT.md
3. Follow the weekly schedule in 03_WEEKLY_SCHEDULE.md
4. Track progress in 04_PROGRESS_TRACKER.md
5. Start with Module 1.1 in modules/ folder

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Remember: This is a marathon, not a sprint. Focus on deep understanding rather than rushing through topics. Happy learning! 🐍