DSA Basics: Complexity Analysis and Recursion
Before any specific data structure makes sense, two questions have to become second nature: how does the work grow as the input grows, and how much memory does that cost. Complexity analysis is the vocabulary interviewers use to ask about trade-offs, and Big-O is how the answer gets written down.
These lessons build that vocabulary visually. Rather than presenting O(n log n) as a formula to remember, the visualizers plot operation counts as the input grows, so the shape of each complexity class is something you have watched rather than memorised. Recursion is covered here too, because the call stack is far easier to reason about once you have seen one drawn.
4
Interactive lessons4
Free to open4
Languages (JS, Python, Java, C++)Basics lessons
Continue the DSA curriculum
Basics is one of 12 topics in the interactive DSA visualizer curriculum. Once this topic is comfortable, the coding interview pattern library reorganises the same material around the patterns interview questions are built on.
Every other DSA visualizer
Data Structures · 28 lessons
- StackFree
- QueueFree
- Deque
- Circular Queue
- Priority Queue (ADT)
- Tree Traversal
- Binary Tree
- Binary Search Tree
- AVL Tree
- Trie (Prefix Tree)
- Segment Tree
- Fenwick Tree
- Red-Black Tree
- Singly Linked List
- Doubly Linked List
- Circular Linked List
- Graph RepresentationFree
- Breadth/Depth First SearchFree
- Depth First Search
- Topological Sort
- Cycle Detection
- Connected Components
- Bipartite Graph
- Dijkstra's Algorithm
- Bellman-Ford Algorithm
- Minimum Spanning Tree
- Floyd-Warshall Algorithm
- Bridges & Articulation Points
