M01Weeks 1-4
Where data comes from
- Design a survey that does not lead the answer
- Sampling, and who gets left out
- Tally sheets to spreadsheets
- Record keeping you can hand to a stranger
All specialisations
Specialisation · 02/16
Data Science and Analytics
“The habit of asking what the numbers are not telling you.”
Levels
3
Primary · Middle · Senior
Outcomes
5
Skills children walk away with
Pathways
4
Future careers unlocked
Quick answer
Data Science and Analytics at NASCA is a hands-on, project-led specialisation for Primary, Middle, Senior. Students learn Collect data properly, Clean and structure it, Visualise honestly and explore careers in Data Scientist, Business Analyst, Statistician. The four-stage journey runs across a full academic year and is World STEM Federation accredited.
Grade 4–12: Collect it, clean it, question it, and only then draw the graph.
The idea
Data science runs alongside the AI stream on purpose. Children learn to gather their own data before they ever touch someone else's, because a child who has cleaned a messy spreadsheet never again trusts a chart blindly. The progression moves from tally sheets and pictographs in primary, through averages, spread, sampling and correlation in middle school, to Python, Pandas and statistical testing at senior level. The recurring question in every lesson is the same one professionals ask: what does this dataset leave out, and who does that harm?
The journey
A four-stage arc
01
Count
Collect your own data and feel how messy it is.
02
Clean
Fix, label and structure it until it can be trusted.
03
Read
Summarise, plot, compare and question the result.
04
Argue
Defend a conclusion with evidence in front of a panel.
Signature project
Flagship build
The School Data Report
A term-long study of something real on campus, canteen waste, bus timings, library use, published with the raw data attached.
Why it matters
Every argument a child will meet in adult life arrives with a chart attached. Children who have collected, cleaned and questioned their own data can tell a real finding from a decorated opinion, which is a life skill long before it is a career skill.
A typical session
- 01Open with a chart from this week's news
- 02Ask what the chart hides
- 03Hands-on: collect or clean a real dataset
- 04Plot it two honest ways and one misleading way
- 05Defend one conclusion out loud
The curriculum
What they actually learn
Six modules across an academic year. Every module is hands-on, project-led and ends with something children have built and can show.
M02Weeks 5-9
Cleaning the mess
- Missing values, duplicates and typos
- Units, dates and the errors they cause
- Structure a table so a machine can read it
- Document every change you make
M03Weeks 10-15
Summarising honestly
- Mean, median, mode and when each one lies
- Spread, outliers and why the average hides them
- Charts that inform and charts that mislead
- Write one sentence the data actually supports
M04Weeks 16-22
Python and Pandas
- Load a real dataset in Colab
- Filter, group and aggregate
- Plot with Matplotlib
- Reproducible notebooks another student can run
M05Weeks 23-27
Asking harder questions
- Correlation, and why it is not cause
- Compare two groups fairly
- Simple hypothesis testing
- Confidence, uncertainty and honest language
M06Weeks 28-30
The published study
- Pick a real question on campus
- Collect, clean, analyse, write
- Publish with the raw data attached
- Take questions from a live panel
Showcase moments
Three highlights through the year
- Term 1
The first honest chart
Children present a finding and the limitation that sits beside it.
- Term 2
Notebook review
Students run each other's Colab notebooks and check the result reproduces.
- Term 3
The school data report
A full study published with methods, data and conclusions.
For parents
Your child will start questioning statistics on the news at dinner. That is the point, and it does not go away.
For teachers & schools
No prior statistics needed. The progression is built so a teacher learns alongside the class in year one.
What children build
- Class surveys
- Cleaned datasets
- Interactive dashboards
- Correlation studies
- Statistical reports
Tools & tech
SpreadsheetsPythonPandasMatplotlibGoogle ColabPublic open-data portals
Levels offered
PrimaryMiddleSenior
Outcomes
What they walk away with
01
Collect data properly
02
Clean and structure it
03
Visualise honestly
04
Test a hypothesis
05
Spot a misleading chart
Questions parents ask
FAQ
The honest answers to the questions families ask us most.
Is this just maths again?
No. Maths class teaches the tools. Here children use them on data they collected themselves, and argue about what it means.
Does my child need to code?
Not to begin. Primary works on paper and in spreadsheets. Python arrives around middle school, once the thinking is in place.
How is it assessed?
On the same six parameters as every NASCA stream: concept, application, structure, development, programming and analytical skills, and presentation.
What if the study finds nothing?
Then that is the result, and it is marked as a good one. Honest negative findings score higher here than decorated ones.