DSA, Made Visual.
An interactive computer science laboratory. Algorithms shouldn't remain abstract lines in textbooks. See their mechanics run, control every step, examine live variable states, and connect logic directly to source code.
From Observation to Mastery
Our five-stage learning progression bridges mathematical theory and practical code comprehension.
Visual Execution
Watch pointers traverse arrays, values exchange during swaps, and minimums lock into position.
Total Playback Control
Play, pause, advance single steps forward, rewind mistakes backward, and adjust execution speeds.
Synchronized Code
Follow the active line in authentic Python code synchronized with live variable inspectors.
Custom Ingestion
Input custom numbers, test boundary duplicates, negative integers, and reverse-sorted sequences.
Selection Sort Visualizer
Experience the core mechanics of Selection Sort: step through quadratic comparisons, watch the minimum index candidate shift as the inner scanner iterates, and witness physical position swaps placing values into their sorted prefix.
Browse Algorithm Disciplines
From fundamental array manipulations to complex dynamic programming state graphs.
Sorting
Systematic algorithms for arranging elements in specified numerical or lexicographical order.
Searching
Target location techniques across linear, sorted, and indexed structures.
Arrays
Contiguous memory representations, two pointers, sliding window, and in-place transformations.
Linked Lists
Singly, doubly, and circular pointer chains with dynamic re-linking.
Stacks
LIFO evaluation engines, monotonic stacks, and parentheses matching.
Queues & Deques
FIFO buffers, circular queues, and priority structures.
Trees & BSTs
Hierarchical traversals, self-balancing rotations, and subtree decomposition.
Graphs
BFS, DFS, Dijkstra shortest path, topological sorting, and minimum spanning trees.
Dynamic Programming
Optimal substructure memoization, tabular state transitions, and space optimization.
Recursion
Call stack frames, base condition resolution, and tree recursion unwinding.
Greedy Algorithms
Locally optimal choice heuristics that construct globally optimal solutions.
Backtracking
Systematic constraint-satisfaction exploration with pruning and state reversal.