Python Roadmap (Beginner → Advanced) - Textnotes

Python Roadmap (Beginner → Advanced)

The Python Roadmap provides a step-by-step learning path from beginner to advanced levels, covering Python fundamentals, data structures, OOP, file handling, web development, automation, data science, and real-world projects. It helps learners quickly understand what to study, build strong coding skills, and become job-ready in Python programming.

1. Basics of Python (Beginner)

  1. Objective: Understand Python syntax, variables, and basic programming constructs.
  2. Topics:
  3. Introduction to Python: What is Python, Features
  4. Installing Python & IDE setup (VS Code, PyCharm, Jupyter Notebook)
  5. Python Syntax, Comments, Indentation
  6. Variables & Data Types: int, float, str, bool, None
  7. Basic Operators: +, -, *, /, %, **, //, comparison operators
  8. Input/Output: input() and print()
  9. Type conversion: int(), str(), float()

2. Control Flow

  1. Objective: Learn to control the execution flow using conditions and loops.
  2. Topics:
  3. Conditional Statements: if, elif, else
  4. Loops: for and while
  5. Loop Control Statements: break, continue, pass
  6. List comprehensions & dictionary comprehensions

3. Functions & Modules

  1. Objective: Write reusable code and organize Python programs.
  2. Topics:
  3. Defining functions using def
  4. Function arguments: positional, keyword, default, arbitrary *args, **kwargs
  5. Return values
  6. Lambda functions
  7. Built-in functions: len(), range(), type(), enumerate()
  8. Modules & Packages: Importing modules (import, from ... import ...)
  9. Standard library overview: math, random, datetime, os, sys

4. Data Structures

  1. Objective: Handle and manipulate data efficiently.
  2. Topics:
  3. Lists: creation, indexing, slicing, methods (append(), pop(), extend())
  4. Tuples: immutability, unpacking
  5. Sets: unique elements, set operations
  6. Dictionaries: key-value pairs, methods (get(), keys(), values(), items())
  7. Strings: indexing, slicing, formatting, string methods
  8. Advanced: Nested data structures

5. Object-Oriented Programming (OOP)

  1. Objective: Understand classes, objects, and OOP concepts.
  2. Topics:
  3. Classes & Objects
  4. Attributes & Methods
  5. __init__() constructor
  6. Encapsulation: private/public attributes
  7. Inheritance & Polymorphism
  8. Method Overriding
  9. Special Methods: __str__(), __repr__(), __len__(), etc.

6. Exception Handling

  1. Objective: Write robust programs by handling errors gracefully.
  2. Topics:
  3. Errors in Python: Syntax vs Runtime
  4. try, except blocks
  5. else and finally
  6. Raising exceptions using raise
  7. Custom exceptions

7. File Handling

  1. Objective: Work with files for reading/writing data.
  2. Topics:
  3. File modes: r, w, a, rb, wb
  4. Reading files: read(), readline(), readlines()
  5. Writing files: write(), writelines()
  6. Working with JSON: json module

8. Python Libraries (Intermediate)

  1. Objective: Learn popular libraries for practical tasks.
  2. Topics:
  3. NumPy: arrays, vectorized operations
  4. Pandas: DataFrames, Series, CSV/Excel operations
  5. Matplotlib / Seaborn: Data visualization
  6. Requests: HTTP requests, APIs
  7. BeautifulSoup / Scrapy: Web scraping

9. Advanced Python Concepts

  1. Objective: Master Python for larger projects.
  2. Topics:
  3. Iterators & Generators: iter(), next(), yield
  4. Decorators: function decorators, class decorators
  5. Context Managers: with statement
  6. Regular Expressions: re module
  7. Multithreading & Multiprocessing
  8. Logging and debugging

10. Working with Databases

  1. Objective: Store and retrieve data using Python.
  2. Topics:
  3. SQLite / MySQL / PostgreSQL connection using sqlite3, SQLAlchemy
  4. CRUD operations
  5. ORM basics

11. Web Development with Python

  1. Objective: Build web applications.
  2. Topics:
  3. Flask Basics: Routing, Templates, Forms
  4. Django Basics: Models, Views, Templates
  5. REST APIs with Flask/Django REST Framework

12. Automation & Scripting

  1. Objective: Automate repetitive tasks.
  2. Topics:
  3. Web scraping automation
  4. Excel automation with openpyxl / pandas
  5. PDF automation with PyPDF2
  6. Sending emails: smtplib

13. Testing & Best Practices

  1. Objective: Write reliable and maintainable code.
  2. Topics:
  3. Unit Testing: unittest, pytest
  4. Code formatting: PEP8, Black
  5. Version control with Git/GitHub
  6. Virtual environments: venv, pipenv

14. Python for Data Science & AI (Optional Advanced Track)

  1. Objective: Use Python for data analysis and machine learning.
  2. Topics:
  3. Data analysis: Pandas, NumPy
  4. Visualization: Matplotlib, Seaborn, Plotly
  5. Machine Learning: scikit-learn
  6. Deep Learning: TensorFlow, PyTorch
  7. NLP: NLTK, spaCy

15. Projects for Practice

  1. Beginner: Calculator, To-Do App, Dice Rolling Simulator
  2. Intermediate: Web Scraper, REST API, Data Analysis Project
  3. Advanced: Chatbot, E-commerce Website, Machine Learning Model Deployment