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Big O and Complexity

2 lessonsAbout 12 minutesBeginner

This part of the DSA Tutorial runs from Big O Notation Explained through to Time vs Space Complexity. There are 2 lessons here, and working through them takes about 12 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.
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