Unit 2: Data Science and Representation (Pyret)

Framing Concept: Data is a constructed lens on the world.

Unit Overview

In this unit, students transition from function-based design to working with structured data. Using Pyret and Bootstrap’s Data Science tools, students learn to filter, transform, and visualize real-world datasets. We treat data not as neutral, but as a model—a system of choices, omissions, and interpretations. The goal is to build technical skill and analytical power: students should be able to manipulate data and ask, “What is this data saying—and not saying—about the world?”

Essential Questions

  • What does it mean to model a world with data?
  • How do we clean, filter, and transform data programmatically?
  • What categories exist in data, and who defines them?
  • What is the relationship between structure and interpretation?

Core Learning Goals

  • Read, query, and transform datasets using Pyret
  • Use filter, map, and build-column to explore collections
  • Create meaningful visualizations (bar graphs, pie charts, etc.)
  • Analyze bias, omission, and representation in data design
  • Connect computational work to civic and institutional structures

Core Activities

  • Investigate a museum dataset (e.g., the Met): Who’s represented?
  • Explore how filters change what we see
  • Design a “Re-curated Exhibit” based on a new interpretation
  • Mini-lectures on categories, aggregation, and curation logic
  • Graph interpretation and critique as a weekly routine

Transfer and Syntax Shift

Students move from Racket to Pyret syntax, but retain functional reasoning. This transition emphasizes that conceptual fluency transcends syntax. It also stresses the need for reading documentation and understanding new environments—skills essential to any computational future.

Stretch + Extensions

  • Students bring in their own dataset of interest (music, books, school policies)
  • Join datasets to explore intersections (e.g., geography + art)
  • Use computed columns to create new derived fields
  • Optional introduction to categorical encoding and sorting

Assessment and Reflection

  • Annotated visualization: What do you see, and why?
  • Data diary: What decisions did you make to prepare and clean your dataset?
  • Weekly journal: Where else do you see filtered or curated data in your life?
  • Vocabulary check: “record,” “field,” “filter,” “category,” “visualization”

End-of-Unit Statement

By the end of Unit 3, students understand that data is never “raw.” They see that every data set reflects a worldview. They have learned how to ask meaningful questions, shape their data to reflect those questions, and communicate the results clearly. They are now ready to re-engage with control flow and state in a new language—bringing their structure and insight with them.