Python learning programme based on A Byte of Python (https://python.swaroopch.com/)
The picture of you, not of the topic. Each statement is something you'll be able to do by the end.
I can Implement and Optimize Python Programs
I can Utilize Object-Oriented Programming in Python
I can Manage Data and Resources Effectively in Python
I can Debug and Test Python Programs
The path through the material. Each lesson tackles one essential question.
What does it take to turn calculations and choices into a program you can run again and again?
Why do ordered structures and tuple packing let programs pass multiple values cleanly between parts?
How does formatting let code turn raw values and expressions into clear, trustworthy messages for the user?
How does Python's model of values and containers shape both how information is stored and how text is rendered?
How do loops and built-in collections work together to transform data efficiently?
How does a clear design drive the choice of loop structures and exits that make programs correct and readable?
How can we steer a loop’s flow while using functions to encapsulate decisions into clean, reusable units?
How can thoughtful parameter design make functions and methods intuitive to call and adaptable to varied use cases?
How does purposeful documentation turn code into a readable conversation between authors and future readers?
How do modules and introspection let us find and assemble existing capabilities instead of reinventing them?
How does modeling data and behavior with classes lead to objects that feel like natural building blocks of a program?
How does the way we scope names and derive classes shape what code can see and how behavior is specialized across a program?
Why do file modes and character encodings matter when programs move text to and from disk, and how do they protect meaning across languages and platforms?
How does rapid, incremental exploration help us shape correct programs, from sketching tiny functions to catching syntax mistakes and confirming behavior early?
How do we leverage Python's execution model and built-in documentation to find the right tools and assemble small, practical programs that do real work?
What makes a file an importable module versus a standalone script, and how do search paths, command-line arguments, and execution context govern that boundary?
Why do persistence and logging turn one-off scripts into maintainable systems, and how do they help us explain, reproduce, and debug program behavior over time?
How can we design code to communicate errors clearly and guarantee resources are released, so programs stay correct and recoverable under real-world failures?
Three ways to connect: Claude Code (PAT + install command), Claude Desktop (.mcpb download — no token to paste), or Claude web (Customise → Connectors → Add custom connector, OAuth). Same MCP endpoint, same identity on every path.
https://nebular.live/api/v1/mcp/