Search and sort
Search and sort algorithms are implemented and timed personally, so the difference stops being theory and becomes a measured graph.
Python for Kids · Class 10 · Module 2 of 9
Algorithms and Complexity
Same answer, wildly different cost. Choosing well is engineering.
8 sessionsSessions 9–16
4 weeksTypical pace
Intermediate to AppliedLevel
Class 10Python & Coding
The big idea
Students implement and measure searching and sorting algorithms, develop Big-O intuition for how cost grows with input size, and learn that every optimisation trades something away.
Why it matters
Complexity reasoning appears in every serious computer science course and in every technical interview a student will ever sit.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Big-O intuition
Big-O describes growth, not speed. O(n²) means doubling the input roughly quadruples the work — regardless of how fast your laptop is.
Trade-offs
Trade-offs are the real lesson: faster often means more memory, more complexity or more assumptions about the data.
Did you know?
Python’s built-in sort, called Timsort, was invented by Tim Peters in 2002 and is now used inside Java, Android and V8 as well.
Common mix-up
“The lowest Big-O wins.” For small inputs, a simple O(n²) sort often beats a complex O(n log n) one. Constants matter in reality.
Session by session
Your 8-session journey
Sessions 9–16 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 search and sort.
- 2
Search and sort
Guided teaching on search and sort, worked through together with the teacher.
- 3
Search and sort — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Big-O intuition
Guided teaching on big-o intuition, building directly on the previous two sessions.
- 5
Big-O intuition — practice lab
Applied tasks that combine search and sort and big-o intuition in one piece of work.
- 6
Trade-offs
Trade-offs introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Algorithm comparison lab.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Algorithm comparison lab
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 16.
Try this at home
Time bubble sort and Python’s built-in sort on 1,000, 10,000 and 100,000 items. The gap will surprise you.
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 2 can:
- Explain and use search and sort without prompting
- Explain and use big-o intuition without prompting
- Explain and use trade-offs without prompting
- Build and finish algorithm comparison lab
- 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:
- Big-O
- Notation describing how cost grows with input size.
- complexity
- The resource cost of an algorithm.
- trade-off
- Gaining one property at the expense of another.
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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