Clean data
Cleaning data first is non-negotiable. A single stray value can drag an average far away from the truth.
Python for Kids · Class 8 · Module 7 of 9
Data Visualisation
A number on its own is a fact. A chart is an argument.
8 sessionsSessions 49–56
4 weeksTypical pace
Foundation to IntermediateLevel
Class 8Python & Coding
The big idea
Students clean a dataset, choose a chart that suits the question, and then write the sentence the chart actually supports — including learning to notice when it does not support the sentence they hoped for.
Why it matters
Reading and making honest charts is a life skill. It is also excellent protection against the misleading graphics students meet online daily.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Basic charts
Basic charts each answer a different question: bar for comparing, line for change over time, pie only for parts of one whole and only when there are few parts.
Explain findings
Explaining findings means stating what the data shows, what it does not show, and what you would need to be more certain.
Did you know?
Truncating a y-axis so it starts at 90 instead of 0 can make a 2% difference look enormous. It is the most common trick in misleading news graphics.
Common mix-up
“The chart proves it.” A chart shows a pattern. Proving a cause needs far more than a correlation on a screen.
Session by session
Your 8-session journey
Sessions 49–56 of the 72-session year, at two one-hour sessions per week.
- 1
Warm-up and big picture
Where this module fits, what you will build, and a hands-on starter that gets everyone curious about clean data.
- 2
Clean data
Guided teaching on clean data, worked through together with the teacher.
- 3
Clean data — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Basic charts
Guided teaching on basic charts, building directly on the previous two sessions.
- 5
Basic charts — practice lab
Applied tasks that combine clean data and basic charts in one piece of work.
- 6
Explain findings
Explain findings introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Student-interest data story.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Student-interest data story
Every module ends with something the student built themselves and can demonstrate. This is the piece that goes into their portfolio and gets explained out loud at the end of session 56.
Try this at home
Find a chart online and redraw it honestly with a zero baseline. Note how much of the drama disappears.
It is finished when
- It works from start to finish without breaking
- You can explain every part of it in your own words
- You tested it and improved at least one thing afterwards
- Someone else used or understood it without your help
By the end of this module
Students finishing Module 7 can:
- Explain and use clean data without prompting
- Explain and use basic charts without prompting
- Explain and use explain findings without prompting
- Build and finish student-interest data story
- Test your own work and correct what you find
- Talk an adult through what you made and why
Word bank
The vocabulary introduced here, in plain language:
- dataset
- A structured collection of related data.
- axis
- The scaled reference line on a chart.
- outlier
- A value far away from the rest of the data.
Module challenge
Think you have got this?
0 XPLevel 1 · Curious Beginner
6 quick questions drawn from this module — vocabulary, the project you build, and a myth-or-fact round. Every wrong answer explains itself, so a mistake still teaches you something.
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