Python is one of the most versatile and beginner-friendly programming languages in the world. From web development and automation to data science and AI, Python forms the backbone of modern software development.

This syllabus is designed as a structured learning roadmap, not just a list of topics. It focuses on building strong fundamentals, clear thinking, and professional-level coding skills.

This guide covers:

  • Python fundamentals from scratch
  • Core programming concepts
  • Advanced Python features
  • Career-oriented applications
  • Interview and professional preparation

Beginner Level — Python Foundations

1. Introduction to Python

  • What is Python
  • History and features of Python
  • Applications of Python
  • Python vs other languages
  • Installing Python
  • Python versions
  • Running Python programs
  • Python interactive shell
  • Writing your first Python program

2. Python Basics & Syntax

  • Python keywords
  • Identifiers
  • Indentation and code structure
  • Comments (single-line and multi-line)
  • Variables and naming conventions
  • Data types overview
  • Type checking using type()
  • Type conversion

3. Input, Output & Operators

  • Input using input()
  • Output using print()
  • Formatted output
  • Arithmetic operators
  • Relational operators
  • Logical operators
  • Assignment operators
  • Operator precedence

4. Control Flow

Conditional Statements

  • if
  • if-else
  • elif
  • Nested conditions

Looping Statements

  • for loop
  • while loop

Loop Control

  • break
  • continue
  • pass

Intermediate Level — Core Python Concepts

5. Strings

  • String creation
  • Indexing and slicing
  • String methods
  • String formatting
  • Escape characters
  • Immutability
  • String operations

6. Lists

  • Creating lists
  • Indexing and slicing
  • List methods
  • Nested lists
  • Iterating through lists
  • List comprehensions

7. Tuples

  • Creating tuples
  • Tuple operations
  • Tuple unpacking
  • Difference between list and tuple
  • Use cases

8. Sets

  • Set creation
  • Set methods
  • Set operations
  • Difference between set and list
  • Use cases

9. Dictionaries

  • Dictionary creation
  • Accessing elements
  • Dictionary methods
  • Iterating dictionaries
  • Nested dictionaries
  • Real-world use cases

10. Functions

  • Defining functions
  • Function parameters
  • Return values
  • Default arguments
  • Keyword arguments
  • Variable-length arguments (*args, **kwargs)
  • Scope (local and global)
  • Lambda functions

11. Modules & Packages

  • Importing modules
  • Built-in modules
  • Creating custom modules
  • Python packages
  • __name__ == "__main__"
  • Virtual environments

Advanced Level — Professional Python

12. File Handling

  • Reading files
  • Writing files
  • File modes
  • Working with CSV files
  • JSON handling
  • Context managers using with

13. Exception Handling

  • Types of errors
  • try and except
  • else and finally
  • Multiple exceptions
  • Custom exceptions
  • Debugging techniques

14. Object-Oriented Programming (OOP)

  • Classes and objects
  • Constructors
  • Instance and class variables
  • Methods
  • Encapsulation
  • Inheritance
  • Polymorphism
  • Abstraction
  • Magic methods (__str__, __len__, etc.)

15. Advanced Python Concepts

  • List, set, and dictionary comprehensions
  • Iterators and generators
  • Decorators
  • Closures
  • Shallow vs deep copy
  • Memory management
  • Garbage collection

16. Python Standard Library

  • math
  • random
  • datetime
  • collections
  • itertools
  • functools
  • os
  • sys

17. Testing & Debugging

  • Assertions
  • Unit testing
  • Writing test cases
  • Testing frameworks overview
  • Debugging tools
  • Logging

Specialized & Career-Oriented Topics

18. Data Structures & Algorithms with Python

  • Time and space complexity
  • Arrays and strings
  • Stacks and queues
  • Linked lists
  • Trees
  • Graphs
  • Dynamic programming
  • Greedy algorithms
  • Recursion and backtracking

19. Automation & Scripting

  • File and folder automation
  • Web scraping basics
  • Task scheduling
  • Command-line tools
  • Automation projects

20. Python for Web Development

  • Backend fundamentals
  • REST APIs
  • JSON and HTTP
  • Web frameworks overview
  • Request–response cycle

21. Python for Data & AI

  • NumPy basics
  • Pandas basics
  • Data cleaning
  • Basic data visualization
  • Working with datasets

22. Performance & Optimization

  • Code optimization
  • Profiling
  • Efficient data handling
  • Best practices
  • PEP 8 guidelines
  • Type hints

23. Real-World Projects

  • Beginner mini projects
  • Automation projects
  • API-based projects
  • Data-driven projects
  • End-to-end Python applications

24. Interview & Professional Topics

  • Common Python interview questions
  • DSA problem solving
  • Design patterns
  • Code optimization
  • Packaging and distribution
  • Resume and project preparation

25. Python Programming Playlist

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More structured Python learning guides and hands-on roadmaps coming soon.