📋 Teacher Cheat Sheet — Session 1: Data Is Everywhere

Data Science for Young Minds · Grade 5 · Ages 10–11

~60 min Ages 10–11 Session 1 of 8 ND-Friendly

⏱ Session Agenda

Time	Block	What's Happening
0–5	🎯 Hook	"Without thinking too hard — name 5 apps you used this week." List on board, ask: what data did each app collect?
5–18	📖 Lesson 1–2	Primary vs. secondary data · Quantitative vs. qualitative · Structured vs. unstructured
18–38	🕵️ Activity	Personal Data Audit — list every piece of data collected about them in one school day. Partner work first, then class combine.
38–50	📖 Lesson 3–4	Where data lives · The data trail · Who collects data and why
50–56	✍️ Reflection	Students write: "Which piece of data surprised you most? Why does someone collect it?"
56–60	👋 Close	Preview S2: "Next time — how do data scientists ask the right questions?"

Pacing note: The Data Audit is the emotional anchor of this session. Students are often surprised (and sometimes unsettled) by how much data exists about them. Honor that reaction — it's the foundation of the whole course.

📦 Materials Needed

Prepare before class:

Student worksheets (1 per student) Sticky notes (2 colors per student) Pencils Whiteboard or chart paper Optional: printed "data trail" infographic

💡 Tip: Pre-write the audit category headers on the board before class: School Data, App/Device Data, Health Data, Location Data, Shopping/Preference Data.

📚 Key Vocabulary

Primary data — data you collect yourself, directly

Secondary data — data collected by someone else that you use

Quantitative data — data expressed as numbers (counts, measurements)

Qualitative data — data expressed as descriptions, categories, words

Structured data — organized in rows/columns; easy to search

Unstructured data — text, photos, audio; harder to analyze systematically

Data trail — the collection of digital records you leave behind as you use technology

💬 Discussion Questions + Teacher Notes

"What's the difference between data you choose to give and data that's collected without you actively noticing?"
→ Grade book, attendance = collected about you. Survey response = you actively give it. Both are data — but agency differs. This distinction matters in S7 (ethics).
"Is a photo on Instagram primary or secondary data — and for whom?"
→ For the person who took it: primary. For a researcher studying social media: secondary. Same data, different perspective. Multiple right answers are the goal.
"Which is easier to put in a spreadsheet — your test score or your opinion of lunch today? Why?"
→ Test score = structured quantitative. Opinion = unstructured qualitative. This leads naturally into why data types matter for analysis.
"Who benefits from your school attendance data being collected?"
→ School (legal compliance), district (funding), you (record of learning). No single "bad" answer. Introduce the idea that data collection often has multiple stakeholders with different interests.
"Can data collected for one purpose be used for another? Is that a problem?"
→ Preview of S7. Accept all positions. The point is to generate the question, not answer it yet.

🕵️ Data Audit — Setup Guide

Students work in pairs first (10 min), then contribute to a class master list (8 min). The goal is quantity and surprise — not completeness.

Audit Categories to post on board:

School Data: attendance, grades, tardiness, lunch choice, library books, test scores, counselor notes
App/Device Data: screen time, search history, location, app usage, contacts, messages
Health Data: nurse visits, vaccination records, fitness tracker if applicable
Location Data: school bus route, when you arrive/leave, places visited
Shopping/Preference Data: what you watch/read/listen to, purchase history

Key debrief question: "Which category had the most items? Does that surprise you? Who do you think has access to each piece of data?"

🎯 Opening Hook

Ask: "Name 5 apps you used this week." Write student responses on the board. Then for each one, ask: "What information does this app need from you to work?"

→ Maps = location. Gaming = usage time, friends list. Music = listening history, preferences. Each app collects data to function AND to sell/improve products.

Punch line: "Every time you use technology, you leave a data trail — even if you didn't fill out a form."

✍️ Reflection Prompt

Write on board:
"Which piece of data from your audit surprised you the most? Who collects it, and why do you think they collect it?"

6 min quiet writing. Students should name a specific data point and speculate about its purpose — this is inference from evidence, a key S8 skill.

Strong response example: "My school lunch choice surprised me. The cafeteria collects it to order the right amount of food, but it also shows what I eat — which feels more personal than a test score."

🧠 ND-Friendly Tips

Start with the familiar — Apps they use daily are less abstract than "big data." Hook to their own lives before expanding outward.
Partner audit first — Working with a partner before reporting to the class reduces anxiety and generates more ideas through natural conversation.
Normalize uncertainty — "We don't know exactly who has access to what — that's one reason data ethics matters" is a fine answer. Don't require certainty.
Name, don't diagnose emotions — If students seem unsettled by the audit, name it: "This can feel surprising or even a little uncomfortable. That's a completely normal reaction — and it's exactly why this subject matters."
Allow typed reflection — For students who struggle with handwriting, a typed response on any device is equally valid.