What is DSA & Big-O Complexity — Free DSA Tutorial
Learn What is DSA & Big-O Complexity in DSA with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn What is DSA & Big-O Complexity in DSA with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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What is DSA & Big-O Complexity in DSA
A data structure is a way of organising data so it can be used efficiently (an array, a linked list, a tree). An algorithm is a step-by-step procedure to solve a problem (searching, sorting, finding a path). DSA is the study of choosing the right data structure and algorithm so your program runs fast and uses little memory — the exact skill product companies test in coding interviews.
We measure efficiency with Big-O notation, which describes how the running time (or memory) grows as the input size n grows — ignoring constants and machine speed. O(1) is constant time (does not grow), O(log n) grows very slowly (binary search), O(n) grows linearly (one pass), O(n log n) is good sorting, O(n²) is nested loops, and O(2ⁿ) is exponential (usually too slow).
Always analyse the worst case unless told otherwise. Two solutions can both be "correct" but one runs in O(n) and the other in O(n²) — for n = 1,000,000 that is the difference between instant and minutes. Interviewers care as much about the complexity as the working code.
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