Unit 5: Code in the Wild (APIs + Jupyter Notebooks)

Framing Concept: Code lives in systems, documents itself, and pulls from the world.

Unit Overview

This unit brings students into modern computing practice—connecting their conceptual fluency with tools used in data science, research, and exploratory computation. Students write programs that reach out into the web (via APIs), and they document and explain their reasoning in Jupyter notebooks. The emphasis is on real-world inquiry: code becomes a tool for investigation, not just execution.

Essential Questions

  • How do programs communicate with other systems?
  • What does it mean to structure a computational investigation?
  • What makes code interpretable by others?
  • How do you prove what your code says?

Core Learning Goals

  • Understand and explain how API requests retrieve structured data
  • Write Python code inside a Jupyter notebook with markdown annotations
  • Combine narrative, code, and output in a coherent investigation
  • Use JSON or CSV APIs to gather external information
  • Communicate the intent, method, and result of a computational analysis

Core Activities

  • Met Museum API Inquiry: Use real museum data to explore a research question
  • Notebook Analysis: Pose a question, query data, visualize results, and explain
  • Code + Context Mini-Lectures: What is a request? What is documentation for?
  • Peer Review Gallery: Swap notebooks and check for clarity, structure, and insight

Stretch + Extensions

  • Chain Multiple APIs: (e.g., link geographic or cultural data to art)
  • Real-Time Data: Pull in weather, transit, or social data feeds
  • Markdown Mastery: Use formatting to guide the reader through technical work
  • Export + Present: Convert notebooks into HTML or PDF reports

Technical Notes

  • Environment: JupyterLab, VS Code with notebook extension, or hosted platforms (e.g., Google Colab)
  • Students will need API keys, request headers, and JSON parsing support scaffolded
  • Dataset choice can be scaffolded or open-ended depending on time

Assessment and Reflection

  • Notebook rubric: question clarity, code correctness, narrative explanation
  • Reader walkthrough: peers explain each other’s notebooks as if presenting
  • Final mini-conference: each student defends the “how” and “why” of their investigation
  • Journal: What new kinds of thinking did this unit demand from you?

End-of-Unit Statement

By the end of Unit 6, students are working in real computational environments, handling live data, and writing in a professional format. They are asking questions, building pipelines to answer them, and communicating results that others can read, follow, and critique. This unit launches them into their final project with tools for inquiry, storytelling, and technical literacy.