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Big O and Complexity

3 lessonsAbout 18 minutesBeginner

This part of the DSA Tutorial runs from Big O Notation Explained through to Amortised Analysis. There are 3 lessons here, and working through them takes about 18 minutes at a steady pace.

It follows on from DSA Introduction, so finish that first if you have not already — the examples below assume you are comfortable with it.

Each lesson below says what it covers before you open it. Read them in order the first time; afterwards this page works as a index you can jump back into when you need to check one thing.

  1. 1Big O Notation ExplainedBig O describes how an algorithm slows down as input grows, not how many milliseconds it takes. Learn to read O(1), O(n), O(n log n) and O(n²) from JavaScript code.
  2. 2Time vs Space ComplexityTime complexity counts operations; space complexity counts memory. Learn to measure both in JavaScript and recognise when trading one for the other is worth it.
  3. 3Amortised AnalysisWhy push is called O(1) when it sometimes copies the whole array. Learn amortised analysis in JavaScript and the difference between an average case and an amortised one.
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