DSA Learning Roadmap
A structured path from foundations to advanced patterns. Follow this roadmap to build interview-ready DSA skills with interactive visualizations.
1
Foundations
Understand how to measure algorithm efficiency and how data is represented at the binary level.
2
Core Data Structures
Learn the data structures that form the building blocks of every algorithm. Start with arrays and hash tables, then progress to stacks, queues, and linked lists.
ArraysContiguous memory, indexed accessbeginnerHash TablesO(1) key-value lookupbeginnerStacksLast-In-First-Out (LIFO)beginnerQueuesFirst-In-First-Out (FIFO)beginnerHeaps & Priority QueuesFast min/max access with efficient updatesintermediateTreesHierarchical data and recursionintermediateTrie (Prefix Tree)Fast prefix lookup and word-based searchbeginnerLinked ListsNodes connected by pointersbeginner
3
Algorithms
Master the fundamental algorithmic techniques: graph traversals, recursion, backtracking, and sorting. These form the backbone of interview problem-solving.
4
Problem-Solving Patterns
Learn reusable patterns that apply across dozens of problems. Recognizing these patterns is the key skill interviewers test for.
Two PointersUse two pointers to traverse an array or string, reducing time complexity from O(n^2) to O(n) for problems involving pairs or subarrays.O(n) time · O(1) spaceHash MapUse a hash map for O(1) lookup to track seen elements, frequencies, or complement values during array traversal.O(n) time · O(n) spaceBit ManipulationUse bitwise operations to solve problems efficiently by treating integers as arrays of bits.O(1) or O(log n) time · O(1) spaceBinary SearchEliminate half the search space each iteration by comparing the midpoint against a condition, reducing O(n) linear scans to O(log n).O(log n) time · O(1) spaceSortingUse sorting as a preprocessing step to simplify problems, or apply partition logic for in-place rearrangement. Many interview problems become trivial once the array is sorted.O(n log n) time · O(1) to O(n) spaceHeapUse a heap (priority queue) when you repeatedly need the minimum or maximum of a changing set while also inserting and deleting values efficiently.O(log n) time · O(n) space