Clean tabular data
Clean tabular data first: fix blanks, duplicates and inconsistent labels. Analysis of dirty data produces confident nonsense.
AI for Kids · Class 8 · Module 4 of 9
AI, Data and Patterns
Data does not speak for itself. People make it speak, and sometimes they make it lie.
8 sessionsSessions 25–32
4 weeksTypical pace
IntermediateLevel
Class 8Artificial Intelligence
The big idea
Students clean a real dataset, ask questions of it, use AI to help interpret charts, and practise stating conclusions with an honest level of confidence.
Why it matters
Data literacy is protection. Students are shown statistics daily and are rarely taught how to interrogate them.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Questioning data
Questioning data means asking who collected it, from whom, when and why. Every dataset has a point of view.
Chart interpretation
Chart interpretation means reading the axes and the sample size before reacting to the shape.
Did you know?
Ice-cream sales and drowning incidents rise together. Neither causes the other — hot weather causes both. This is why correlation is not causation.
Common mix-up
“A big sample means a good study.” A large but skewed sample can be far more misleading than a small, careful one.
Session by session
Your 8-session journey
Sessions 25–32 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 tabular data.
- 2
Clean tabular data
Guided teaching on clean tabular data, worked through together with the teacher.
- 3
Clean tabular data — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Questioning data
Guided teaching on questioning data, building directly on the previous two sessions.
- 5
Questioning data — practice lab
Applied tasks that combine clean tabular data and questioning data in one piece of work.
- 6
Chart interpretation
Chart interpretation introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Survey analysis findings poster.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Survey analysis findings poster
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 32.
Try this at home
Survey your year group, then write two honest headlines from the same data — one that oversells it and one that is accurate.
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 4 can:
- Explain and use clean tabular data without prompting
- Explain and use questioning data without prompting
- Explain and use chart interpretation without prompting
- Build and finish survey analysis findings poster
- 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:
- correlation
- Two things changing together.
- causation
- One thing actually causing another.
- sample
- The group actually measured in a study.
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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