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SQ/EN
American Corners

Course

Programming in a Data World

Python from your first steps

Python from first steps to data thinking: Turtle graphics, loops and functions, and a first taste of machine learning.

What this course promises

Pilot
For
Motivated beginners who want to write their first Python programs.
Prerequisites
No programming experience required · Basic keyboard and file skills are helpful
Expected effort
About 3 to 5 hours plus time to build the final game
Tools and materials
A computer with Python 3 and Turtle · A text editor or Python learning environment
Final capability
Build and explain a working Turtle game that combines the course concepts.

By the end, you should be able to

  • Use variables, conditions, loops, functions and lists in small Python programs
  • Predict program behaviour and debug common mistakes
  • Break a program into reusable parts
  • Design, build, test and explain a small Turtle game

Content status: evolving · reviewed 2026-08-21

  1. № 01 · Getting started

    Module checkpoint: produce evidence

    You should be able to

    • • Trace variables and control flow
    • • Use loops to create a visual pattern

    Predict a Turtle pattern before running it. Then run it, compare the result and change one variable to create a deliberate variation.

    Evidence: Your prediction, the result and one explained revision.

    Need a hint?

    Trace one loop iteration at a time and keep angle and distance separate.

  2. № 02 · Reusable code

    Module checkpoint: produce evidence

    You should be able to

    • • Turn repeated code into a function
    • • Choose useful parameters and return values

    Take one repeated drawing or calculation and refactor it into a named function. Test it with two different inputs.

    Evidence: Working code plus one sentence explaining the function boundary.

  3. № 03 · From code to ideas

    Module checkpoint: produce evidence

    You should be able to

    • • Connect code to events, data and simple models
    • • Explain limits rather than treating output as magic

    Choose the cipher or event-driven example. Predict one output, test it, find one limitation and improve the program.

    Evidence: A before-and-after example with an explanation of the change.

  4. № 04 · Build it

    Module checkpoint: produce evidence

    You should be able to

    • • Plan, build, test and explain a small game
    • • Revise a project after observing a failure

    Build the capstone game, ask someone else to try it, record one problem they encounter and revise the game.

    Evidence: A working game, test note and explained revision.

The story

Python, algorithms and an introduction to artificial intelligence.

An introductory Python programming program. From drawing with Turtle through variables, conditionals, loops and functions, students built the foundations of algorithmic thinking step by step.

Through guest workshops with data scientists and researchers, the program connected Python fundamentals to real examples from machine learning and data science.

Continue learning: 0 of 11 lessons are marked complete. The required first-program practice is not yet complete. A page mark alone is not evidence of learning. Practice completion records participation; certificate requests are reviewed by a person.

Your progress is saved on this device only. On iPhone it can be cleared if you don't visit for about a week, so export it to keep it safe.

This course was developed by alphaPlan Center from programs delivered in partnership with the American Corners network.