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Coding · Grade 11

Algorithmic Complexity and Measurement

Learners derive Big-O bounds from code by counting operations through sequences, nested loops with dependent bounds and halving loops, distinguish best, worst and average cases, and check their analysis with exact operation counts and doubling experiments rather than noisy timings. They finish with space complexity and judgement about when complexity really matters.

  • 4 lessons
  • About 100 minutes
  • Check what you learned: 8 questions
  • Ways to answer: choose an answer, trace code, match pairs, type a number, sort into groups and true or false
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What learners will discover

  • Derive Big-O bounds from code by counting operations and keeping the dominant term.
  • Distinguish best, worst and average cases and explain why worst-case bounds are usually quoted.
  • Measure growth empirically with operation counts and doubling experiments, and interpret noisy timings sensibly.
  • Analyse space complexity and time-space trade-offs, and judge when complexity matters in practice.

Lessons

  1. Deriving Big-O from code 6 steps3 practiceabout 24 min
  2. Best, worst and average cases 5 steps3 practiceabout 20 min
  3. Measuring growth empirically 5 steps3 practiceabout 22 min
  4. Space and when complexity matters 4 steps3 practiceabout 18 min
  5. Check what you learned 8 questions at the end

Keep exploring

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Guide for grown-ups