Session 1 — What Do You Notice? Grade 3 Data Science · Ages 8–9 ← → or Space to navigate · F = fullscreen
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Session 1 of 8

What Do You
Notice?

Today we become careful observers — the first skill every data scientist needs.

📊 Data Science for Young Minds · Grade 3
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Today's Plan

What We're Doing Today

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Opening Hook

What Do You Notice?

Your teacher is passing around a bag of objects.
Don't say anything yet — just look. And touch.

What do you notice?

Take 2 minutes. Look carefully. What can you say about what's in the bag?

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Lesson 1

Looking vs. Observing

👁️ Looking

  • Happens automatically
  • Fast, casual glance
  • You might miss details
  • "Yeah, I see it"

🔍 Observing

  • A choice you make on purpose
  • Slow and careful
  • You notice ALL the details
  • "I see it's rough, round, and gray"

Data science starts with careful observation. You can't ask good questions about something you haven't looked at closely.

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Lesson 2

Attributes — Describing Words

An attribute is a specific characteristic of an object.

Color
red, blue, yellow, green…
Size
big, small, medium, tiny…
Shape
round, square, flat, pointy…
Texture
smooth, rough, bumpy, soft…
Weight
heavy, light, medium…
Material
metal, plastic, wood, fabric…

When we describe objects using specific attributes, we're preparing to sort and organize data!

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Lesson 2

Sorting — Same Objects, Different Groups

The same objects can be sorted many different ways depending on which attribute you choose.

Sort by Color
🔴🔴🔵🔵🟡
red / blue / yellow
Sort by Shape
⭕⭕🔷🔷⬛
round / diamond / square
Sort by Size
🔴🟡⭕
small / medium / large

💡 Same objects — completely different groups each time. The rule you choose changes everything.

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Activity Time!

Object Sort Challenge

Your group has a bag of 20+ objects. Your challenge:

⏱ You have 15 minutes. Ready? Go!

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Debrief

What Did You Discover?

"Did your objects end up in different groups each time? What does that tell you about organizing things?"

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🧠
Brain Break — Stand Up Sort!

Your teacher will call out an attribute.
Move to the right side if it describes you,
left side if it doesn't!

Examples: wearing something blue · has laces · round buttons · smooth fabric

You just sorted yourselves like data! 📊

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Lesson 3

Patterns — Rules That Repeat

A pattern is something that repeats or follows a rule. Data scientists look for patterns — it's how they find meaning.

Nature
Seasons repeat every year · Day follows night
School
Same schedule every Monday · Lunch at the same time
Numbers
2, 4, 6, 8… · Every 5th tally mark crosses
Behavior
Most kids pick chocolate milk · Rainy days = more indoor recess

When we find a pattern in data, we can start making predictions — and asking better questions!

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Lesson 4

Observation vs. Opinion

Data science needs observations — not opinions. Can you tell the difference?

✅ Observations (facts you can verify)
  • The leaf is 8 cm long
  • The button has 4 holes
  • The rock is gray and rough
  • The pencil is shorter than my ruler
❌ Opinions (feelings/judgments)
  • The leaf is beautiful
  • The button is cute
  • The rock is boring
  • The pencil is ugly

Observations can be measured, counted, or verified by anyone. Opinions can't.

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Lesson 4

Your Observation Journal

Data scientists keep observation journals — they record what they notice using words, numbers, AND sketches.

📝 Words

  • Specific adjectives
  • Uses attribute language
  • "The coin is circular, silver, and smooth"

✏️ Sketches + Numbers

  • Draw what you see
  • Add measurements
  • "~2cm wide · 4 holes · round"

Your journal is your scientific memory. If you don't write it down, you'll forget!

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Journal Time

Observation Journal — Your Turn

"Choose ONE object from today's sort.
Write 3 observations. Add a sketch.
No opinions allowed!"

📝 8 minutes of quiet writing. Use your worksheet — Part 3.

Check: Are you writing facts or feelings? Would someone else agree with your observation?

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Vocabulary Review

Words to Know

Observation
Noticing details carefully and on purpose
Attribute
A specific characteristic — color, size, shape, texture
Sorting
Grouping objects by shared attributes
Pattern
Something that repeats or follows a rule
Category
A named group that objects belong to
Opinion
A feeling or judgment — NOT an observation
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Wrap Up

Session 1 Complete! 🎉

🔮 Coming up — Session 2: Now that we can observe, how do we turn observations into questions that data can answer?

Take-home: Try your observation journal somewhere at home tonight. What do you notice?