Dataset preparation
Dataset preparation is the unglamorous majority of the work — cleaning, joining, checking and documenting.
AI for Kids · Class 10 · Module 4 of 9
AI for Data Decisions
A dashboard that looks authoritative and misleads is worse than no dashboard.
8 sessionsSessions 25–32
4 weeksTypical pace
AppliedLevel
Class 10Artificial Intelligence
The big idea
Students prepare a dataset properly, design visual analysis that answers a real question, and interrogate their own dashboard for the claims it implies but cannot actually support.
Why it matters
Decision-makers act on dashboards. Designing them honestly is an ethical responsibility, not just a design task.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Visual analysis
Visual analysis means choosing the chart that answers the question, with honest scales and visible sample sizes.
Avoiding false claims
Avoiding false claims means checking what a viewer will conclude, not merely what you technically stated.
Did you know?
A well-designed dashboard is judged by the decisions it improves, not by the number of charts it contains.
Common mix-up
“More metrics means more insight.” Too many metrics hides the important one. Most good dashboards answer three questions well.
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 dataset preparation.
- 2
Dataset preparation
Guided teaching on dataset preparation, worked through together with the teacher.
- 3
Dataset preparation — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Visual analysis
Guided teaching on visual analysis, building directly on the previous two sessions.
- 5
Visual analysis — practice lab
Applied tasks that combine dataset preparation and visual analysis in one piece of work.
- 6
Avoiding false claims
Avoiding false claims introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Evidence-based decision dashboard concept.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Evidence-based decision dashboard concept
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
Show your dashboard to someone for thirty seconds, then ask what they concluded. If it is wrong, the dashboard is wrong.
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 dataset preparation without prompting
- Explain and use visual analysis without prompting
- Explain and use avoiding false claims without prompting
- Build and finish evidence-based decision dashboard concept
- 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:
- dashboard
- A visual summary supporting decisions.
- metric
- A measured value tracked over time.
- data preparation
- Cleaning and structuring data before analysis.
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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