Python Roadmap For Beginners

Why learn this: These are the building blocks of Python programming. Without mastering these concepts, you won’t be able to write functional code or progress to more advanced topics. These fundamentals form the language “grammar” you’ll use in all Python programming. This Python RoadMap is for primarily for beginners!

1. Fundamentals (2-4 weeks)

You can practice using datasets on Kaggle

  • Python Installation & Setup
    • Install Python (3.x recommended)
    • Set up a code editor (VS Code, PyCharm, etc.)
    • Learn to use the command line/terminal

  • Basic Syntax
    • Variables and data types
    • Operators (arithmetic, comparison, logical)
    • Input/output (print, input)
    • Comments

  • Control Flow
    • Conditional statements (if, elif, else)
    • Loops (for, while)
    • Break and continue statements

  • Data Structures
    • Lists
    • Tuples
    • Dictionaries
    • Sets
    • String manipulation

  • Functions
    • Defining and calling functions
    • Parameters and arguments
    • Return values
    • Lambda functions
    • Scope (global vs local)

2. Intermediate Concepts (4-6 weeks)

Why learn this: These concepts help you write more organised, robust, and reusable code. File operations let you persist and retrieve data, error handling makes your programs resilient, modules help structure larger codebases, and OOP provides a powerful paradigm for modelling complex systems. This section goes in tandem with SQL. Here is the corresponding SQL Roadmap

  • File Operations
    • Reading/writing text files
    • Working with CSV, JSON
    • File paths and directories

  • Error Handling
    • Try/except blocks
    • Common exceptions
    • Custom exceptions

  • Modules and Packages
    • Importing built-in modules
    • Creating your own modules
    • Package installation using pip
    • Virtual environments

  • Object-Oriented Programming (Optional)
    • Classes and objects
    • Attributes and methods
    • Inheritance
    • Encapsulation
    • Polymorphism

  • Functional Programming
    • Map, filter, reduce
    • List comprehensions
    • Generator expressions
    • Decorators

3. Advanced Topics (6-8 weeks) For Beginners

Why learn this: These advanced concepts enable you to tackle complex programming challenges with elegant solutions. They help you write more efficient code, handle sophisticated data structures, process data asynchronously, and create more powerful abstractions. These skills separate novice programmers from professionals.

  • Advanced Data Structures [For Software Engineering]
    • Collections module
    • NamedTuple, defaultdict
    • Arrays, queues, stacks
    • Priority queues

  • Regular Expressions
    • Pattern matching
    • Search and replace
    • Grouping

  • Advanced OOP
    • Multiple inheritance
    • Abstract base classes
    • Class decorators
    • Metaclasses

  • Concurrency
    • Threading
    • Multiprocessing
    • Async IO (asyncio)

  • Context Managers
    • With statement
    • Creating custom context managers

  • Iterators and Generators
    • Creating iterators
    • Generator functions
    • Yield statement

4. Libraries & Frameworks (8+ weeks)

Why learn this: Python’s extensive ecosystem of libraries and frameworks is one of its greatest strengths. Rather than reinventing the wheel, these tools allow you to leverage existing solutions for common problems. Each domain (data science, web development, etc.) has specialised libraries that dramatically increase your productivity and capabilities. This Roadmap for Python Beginners now moves to Data Science/Machine Learning and basic understanding of web to be able to scrape data etc

  • Data Science [For Data Roles: Necessary]
    • NumPy (numerical computing)
    • Pandas (data analysis)
    • Matplotlib/Seaborn (data visualization)
    • SciPy (scientific computing)

  • Web Development
    • Flask (lightweight web framework)
    • Django (full-featured web framework)
    • FastAPI (modern, fast API framework)

  • Machine Learning
    • Scikit-learn (traditional ML)
    • TensorFlow/PyTorch (deep learning)
    • Keras (high-level neural networks API)

  • Automation & Scripting [Data Scrapping]
    • Requests (HTTP library)
    • Beautiful Soup (web scraping)
    • Selenium (browser automation)
    • PyAutoGUI (desktop automation)

  • GUI Development
    • Tkinter (standard GUI)
    • PyQt/PySide (cross-platform GUI)
    • Kivy (mobile app development)

5. Best Practices & Tools (Throughout learning)

Why learn this: Writing code that works is only part of the challenge; writing code that is maintainable, readable, and collaborative is equally important. These practices and tools help ensure your code is high quality, well-tested, properly versioned, and easily understood by others (including your future self). Most people might not focus on these but eventually, one should learn these aspects of Python Roadmap despite being beginners

  • Code Quality
    • PEP 8 style guide
    • Linting (pylint, flake8)
    • Type hints (mypy)
    • Docstrings

  • Testing
    • Unit testing (unittest, pytest)
    • Test-driven development
    • Mocking

  • Version Control
    • Git basics
    • GitHub/GitLab

  • Project Management
    • Virtual environments
    • Package management with pip and Poetry
    • Project structure

  • Documentation
    • Sphinx
    • Read the Docs
    • Markdown

6. Projects to Build (Throughout learning)

Why build projects: Theory without practice has limited value. Building real projects forces you to integrate different concepts, solve practical problems, and learn through doing. Projects also build your portfolio, demonstrating your skills to potential employers or clients. Each project should stretch your abilities slightly beyond your comfort zone.

Beginner Projects

  • Command-line calculator
  • To-do list application
  • Simple text-based game
  • Password generator

Intermediate Projects

  • Weather app using an API
  • Personal budget tracker
  • Web scraper
  • Simple blog with Flask

Advanced Projects

  • Real-time chat application
  • Data analysis dashboard
  • Machine learning project with dataset analysis

7. Resources

Why use these resources: Self-learning requires quality materials. These curated resources provide structured guidance, in-depth explanations, and practical exercises. Different resources suit different learning styles—books for comprehensive coverage, online courses for interactive learning, practice platforms for skill reinforcement, and communities for support and troubleshooting.

Books

  • “Python Crash Course” by Eric Matthes
  • “Automate the Boring Stuff with Python” by Al Sweigart
  • “Fluent Python” by Luciano Ramalho
  • “Python Cookbook” by David Beazley & Brian K. Jones

Online Learning

Practice Platforms [Data Structures]

Communities

8. Next Steps & Specialisations

Why consider specializations: Python is used across numerous industries and domains. Once you’ve built a solid foundation, specializing allows you to develop deeper expertise in areas aligned with your interests and career goals. Each specialization opens different career paths and requires mastery of domain-specific libraries, frameworks, and concepts.

  • DevOps with Python
  • Data Engineering
  • Cybersecurity
  • Game Development
  • Embedded Systems/IoT
  • AI/ML Research
  • Quantitative Finance

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