Part 1

This module reinforces essential Middle School (6-8) foundational standards while applying them to rigorous High School (9-12) data science contexts:

Foundational Reinforcement (Grades 6-8):

• CS.MS.CS.01: Analyze the relationship between hardware and software (Reinforced through setting up local Python environments and understanding VS Code).
• CS.MS.DA.01: Store, copy, search, retrieve, modify, and delete information using appropriate file formats (Applied through strict project directory management and relative pathing).

Core High School Standards (Grades 9-12):

• CS.HS.CS.04: Generate guidelines for systematic troubleshooting strategies (Applied to debugging environment errors).
• CS.HS.DA.01: Collect and transform data using computational tools.
• CS.HS.IC.01: Evaluate how computer science impacts social interactions and cultural practices.

This curriculum is designed to be flexible and "hardware-agnostic." Depending on your school's available technology, students will follow one of two paths:

Path A: PC/Mac Lab (Professional Development Environment)
If your classroom has access to PCs or Macs, students will work in local Jupyter Notebooks via VS Code or Anaconda.

• Muggs Lab Reference Spec (Under $600/seat): Mini PC with Ryzen 7 CPU, 790M iGPU, 32GB DDR5 RAM.
• Capability: Powerful enough to run local Large Language Models (LLMs) offline, ensuring data privacy and zero latency.
• Skill Gained: Managing local kernels, file systems, and professional developer workflows.

Path B: Chromebooks (Cloud-Based Data Science)
If students are working on Chromebooks, the entire curriculum is fully compatible with Google Colab.

• Environment: Cloud-hosted Jupyter Notebooks
• Requirement: A standard Google account
• Skill Gained: Collaborative data science, cloud computing, and browser-based development.

Note: Activity instructions include toggles for both local and cloud-based workflows.