Is the Big-O notation — measuring how code scales lesson free?

The interactive Big-O notation — measuring how code scales lesson is part of Pro (₹299/month). The first 5 lessons of the track are free — no account or card needed.

How long does the Big-O notation — measuring how code scales lesson take?

About 12 minutes end-to-end. It's a hands-on lesson — you read a paragraph, edit the code, and run it in the same tab.

Do I need to install Python to try the Big-O notation — measuring how code scales lesson?

No install required. PyRun runs real CPython in your browser via Pyodide (Python compiled to WebAssembly). Works on Chrome, Firefox, Safari, and Edge — desktop or mobile.

What level is the Big-O notation — measuring how code scales lesson?

Advanced level. Assumes comfort with classes, functions, and standard library. Not for first-time Python learners.

Big-O notation — measuring how code scales

Advanced
12 min
DSA
dsa · complexity · interview
Try it

Lesson 1 of 30

In this track

Big-O notation — measuring how code scales12 min
Arrays: the workhorse of DSA10 min
Two Pointers — the interview technique that wins interviews10 min
Sliding Window — the pattern for subarray problems12 min
Hashing — the O(1) lookup that unlocks interviews10 min
Binary Search — O(log n) on sorted data12 min
Stacks — LIFO and the problems it solves10 min
Queues, Deques, and BFS foundations10 min
Recursion — thinking in terms of subproblems12 min
Sorting — merge, quick, and when Python's sorted() isn't enough12 min
Binary Trees — traversals and recursion12 min
Binary Search Trees — ordered lookup in O(log n)13 min
Graph Representations — adjacency list, matrix, edge list11 min
Graph BFS — shortest paths on unweighted graphs12 min
Graph DFS — cycles, components, and back-edges13 min
Topological Sort — ordering DAGs12 min
Dynamic Programming — remembering to not re-compute12 min
House Robber — choice DP in one dimension12 min
Coin Change — unbounded knapsack, minimum coins13 min
Longest Increasing Subsequence — O(n²) DP and the patience trick14 min
Edit Distance — 2D DP on a grid15 min
Heaps + Priority Queues — the O(log n) sorted-ish structure12 min
Tries — the prefix tree behind autocomplete13 min
Union-Find — the near-O(1) connectivity structure13 min
LRU Cache — build the one behind functools.lru_cache14 min
Top-K Problems — heap, sort, or quickselect?12 min
Backtracking — try, recurse, un-try13 min
Greedy — locally optimal, globally lucky12 min
Bit manipulation — arithmetic in binary11 min
String matching — naive, hashed, and 'just use in'11 min

Advanced · 10 minArrays: the workhorse of DSAIn-place vs copy, indexing tricks, and the array patterns interviewers love.Advanced · 10 minTwo Pointers — the interview technique that wins interviewsTwo indices walking through the data. Turns many O(n²) problems into O(n).Advanced · 12 minSliding Window — the pattern for subarray problemsA moving frame over the array. Fixed or variable size. Solves 80% of 'longest / shortest / sum-K' questions.

loading…

Related lessons

Related lessons