Big O Notation from Zero to Interview Ready
Master Big O notation from zero. In this beginner-friendly lesson, we explain what Big O is, why interviewers care, and how to estimate time and space complexity fast. You’ll learn the 5 most common classes — O(1), O(log n), O(n), O(n log n), O(n²) — plus quick rules of thumb. Then we code three tiny demos in JavaScript: max in array, binary search, and Two Sum (brute vs hash map), each with clear complexity talk tracks you can reuse in interviews. Perfect for software engineer and SDET prep.
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Timestamps:
0:00 — What is Big O in plain English?
0:50 — Why do interviewers care about complexity?
1:28 — Which 5 classes matter most: O(1), O(log n), O(n), O(n log n), O(n²)?
3:10 — How do we estimate quickly: loops, halving, sorting, and maps?
4:12 — Demo 1: how do we find the max in O(n)?
5:25 — Demo 2: how does binary search achieve O(log n)?
6:58 — Demo 3: Two Sum — why O(n²) vs why O(n) with a hash map?
9:15 — What pitfalls inflate complexity (and how to avoid them)?
10:40 — How should you speak time and space out loud in interviews?
12:20 — What mini checklist helps under pressure?
13:30 — What’s the one-minute recap you should remember?
14:11 — End
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