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Session 8 · Capstone

Your Data Story

Grade 4 Data Science · Final Project

Today You Are the Data Scientist

You will ask a question, collect real data from your classmates, organize it, build a chart, and write a complete data story — all in one session.

📝
PLAN
0–8 min
📋
COLLECT
8–25 min
🗂️
ORGANIZE
25–35 min
📊
VISUALIZE
35–47 min
✍️
WRITE
47–57 min
2/15
Review

The Data Cycle — All 8 Sessions

Look how far you've come. Today you use every skill from the course.

S1
Data Tells a Story
S2
Types of Data
S3
Better Surveys
S4
Organizing Data
S5
Averages
S6
Trends Over Time
S7
Comparing Groups
S8
Your Data Story

Today you will design a question, decide what type of data to collect, write a clear survey question, organize the results, choose the right graph, and write a story with a claim and evidence. That is the full data cycle.

3/15
Phase 1

📝 Plan — Choose Your Question

You have 8 minutes

Write your data question in the PLAN box on your planner. Get it approved before you start collecting.

Strong Questions

  • "How many hours of sleep did you get last night?"
  • "What is your favorite school subject?"
  • "How many books have you read this month?"
  • "How do you get to school?"

Avoid These

  • Yes/no questions — not enough data variety
  • Questions with more than 6 answer choices
  • Questions about things from weeks ago
  • Private or sensitive topics

→ Ask your teacher to approve your question before moving to Collect.

4/15
Phase 2

📋 Collect — Survey 10+ Classmates

You have 17 minutes

Walk around the room and ask at least 10 classmates your question. Record each answer with a tally mark or number in your COLLECT box.

1
Walk up to a classmate
2
Ask your question clearly
3
Record their answer
4
Thank them, move on
5
Repeat for 10+ people

Tally Tip

For categorical data: use tally marks next to each answer choice. For numerical data: write each number down in order as you collect it — you'll need to sort it in the Organize phase.

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Phase 3

🗂️ Organize — Total & Analyze

You have 10 minutes

Count up your tallies, find totals, and answer the analysis questions in your ORGANIZE box.

For Categorical Data

  • Count tally marks for each category
  • Find the most common response
  • Find the least common response
  • Double-check totals add up to your sample size

For Numerical Data

  • Put numbers in order (smallest to largest)
  • Find the minimum and maximum
  • Calculate the range
  • Find the mean or median if you have time

→ If your totals don't add up, recount — data accuracy matters!

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Phase 4

📊 Visualize — Build Your Chart

You have 12 minutes

Choose the right chart type and build it carefully in your VISUALIZE box. Add a title, axis labels, and scale.

📊
Bar chart → categories
📈
Line graph → data over time
•••
Dot plot → numerical spread

Chart Checklist

Title that describes the data
X-axis labeled with categories or values
Y-axis labeled and starting at 0
Scale is consistent (equal spacing)
All bars/points are accurate to your data

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Phase 5

✍️ Write — Tell Your Data Story

You have 10 minutes

Write at least 3 sentences that tell the story of your data. Every sentence should include real numbers from your data.

My question was: "___." I surveyed ___ people.
The most common answer was ___, chosen by ___ out of ___ people.
I was surprised that ___ because I expected ___.
My data shows that most people ___, which means ___.
If I could survey more people, I would want to find out ___.

→ Strong writers: use all 5 frames. Aim to have at least one number in each sentence.

8/15
Exemplar

What a Strong Data Story Looks Like

Sample Student Data Story — "Favorite School Subject"

My question was: "What is your favorite school subject?" I surveyed 12 classmates.

The most common answer was Math, chosen by 5 out of 12 people. Science came second with 4 students, and Reading had 3 students.

I was surprised that no one chose Social Studies, because I expected at least 1 or 2 people to pick it.

My data shows that most students in our class prefer Math or Science, which means they may enjoy subjects with problem-solving.

If I could survey more people, I would want to find out whether students in other grade levels have different favorite subjects.

Notice: every sentence has specific numbers, and the last sentence asks a new question — that's what real scientists do.

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Share

Gallery Walk — Sharing Protocol

How It Works

Post your project on your desk or the wall. Walk around and look at 2–3 classmates' data stories. Leave a sticky note or use this sentence frame:

"I noticed your data shows ___, which is interesting because ___."
"Your chart made it easy to see ___ because ___."
"One question I have about your data is ___."

Gallery Walk Rules

Walk quietly · Read before commenting · Only positive and curious feedback · Every project deserves attention

10/15
Reflection

What Makes a Good Data Question?

A Good Question Is...

  • Answerable with data you can actually collect today
  • Interesting enough that you want to know the answer
  • Clear — people understand it the first time they hear it
  • Specific — not too broad or vague
  • Neutral — doesn't lead people toward one answer

A Weak Question Is...

  • Unanswerable in one session
  • So broad that you can't pick the right graph
  • Confusing or double-barreled
  • Leading or loaded
  • Has only yes/no answers

The hardest part of data science is often choosing the right question. Every scientist revises their question at least once before collecting data.

11/15
Writing Skill

Claim + Evidence in Your Story

From Session 1 — Now Applied to Your Own Data

In Session 1 we learned that a claim needs evidence. In your data story, every claim you make must be backed by a number from your chart.

Claim without evidence (weak)

"Most students like Math."

Claim with evidence (strong)

"Most students like Math — 5 out of 12 students surveyed chose it as their favorite subject, more than any other option."

→ Go back and check your writing. Does every claim have a number attached to it?

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Big Picture

You Just Did What Real Data Scientists Do

Asked a question
📋
Collected evidence
🗂️
Organized findings
📊
Visualized patterns
✍️
Communicated results

Real-World Data Scientists Do This Every Day

Doctors ask: "Which treatment works better?" and study patient data.
Sports coaches ask: "Which plays score the most points?" and study game data.
City planners ask: "Where do people walk most?" and study traffic data.

The only difference is the scale — they survey thousands instead of 12.

13/15
Brain Break

Stand Up — Data Scientist Edition

🔬 Quick Data Survey Right Now

Stand up if your data question was about categorical data.
Stay seated if your question was about numerical data.

Look around — that's real data about our class right now!

Now: stand if you chose a bar chart · stay seated for any other chart type.

What does this quick survey tell us about what questions our class asked?

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Next Steps

Where Data Science Goes From Here

Grade 5 Preview

  • Larger data sets (100+ values)
  • Scatter plots and two-variable data
  • Probability and predictions
  • Comparing data from different sources
  • Evaluating other people's data claims

Keep Practicing

  • Notice charts and graphs in news and books
  • Ask "what data supports this claim?" when you read something
  • Collect data about your own life — habits, times, counts
  • Try to spot when a graph might be misleading

Every time you look at a chart, ask three questions: What does it show? What does it not show? What would I need to know more?

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Wrap Up

Grade 4 Data Science — Complete!

🏆
You completed 8 sessions of data science
📊
You built real charts from real data
✍️
You wrote evidence-based claims
🔍
You asked your own question and answered it

Exit Ticket

On the back of your planner, complete this sentence:

"The most interesting thing I discovered in my data was ___, and it made me wonder ___."

Grade 4 Data Science · Session 8 · Data Story Capstone · sdabagh.github.io