CSV and JSON
CSV and JSON are the two formats most real data arrives in, and moving cleanly between them is a routine daily task.
Python for Kids · Class 10 · Module 4 of 9
Data Engineering Basics
Real data is messy, incomplete and occasionally lying. Build for that.
8 sessionsSessions 25–32
4 weeksTypical pace
Intermediate to AppliedLevel
Class 10Python & Coding
The big idea
A pipeline moves data through defined stages — load, clean, validate, transform, output — with each stage separately testable, so the same process can be rerun on new data with confidence.
Why it matters
Pipeline thinking underpins data science, analytics and machine learning. Reproducibility is what makes results trustworthy.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Clean and validate
Cleaning and validation handle blanks, duplicates, wrong types and impossible values — and record what was rejected rather than silently dropping it.
Transform pipeline
A transform pipeline chains reusable stages, so the whole process can be rerun on tomorrow’s data without editing anything.
Did you know?
Industry surveys consistently find data professionals spend most of their time cleaning and preparing data, not analysing it.
Common mix-up
“Just delete the bad rows.” Sometimes correct, often not. Silently dropping data can change a conclusion completely — always log what was removed.
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 csv and json.
- 2
CSV and JSON
Guided teaching on csv and json, worked through together with the teacher.
- 3
CSV and JSON — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Clean and validate
Guided teaching on clean and validate, building directly on the previous two sessions.
- 5
Clean and validate — practice lab
Applied tasks that combine csv and json and clean and validate in one piece of work.
- 6
Transform pipeline
Transform pipeline introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Reusable data pipeline.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Reusable data pipeline
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
Build a pipeline for a genuinely messy public dataset, then run it on a second dataset without changing the code.
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 csv and json without prompting
- Explain and use clean and validate without prompting
- Explain and use transform pipeline without prompting
- Build and finish reusable data pipeline
- 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:
- pipeline
- A chain of processing stages applied to data.
- schema
- The expected structure and types of data.
- reproducible
- Able to be rerun with the same results.
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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