NeetCode 150 Core Roadmap
Organized across 7 hierarchical tiers and 18 topic nodes with explicit prerequisite dependencies. Select a Curated List or a Company to fill this roadmap with targeted problems.
Hash maps, sets, frequency counting, and prefix arrays. The universal prerequisite for all algorithm interviews.
Converging and sliding indices on sorted arrays. Eliminates quadratic brute-force loops down to O(n).
LIFO structures, parentheses validation, and monotonic stacks for next-greater-element queries.
Fixed and dynamically expanding subarray/substring windows with state preservation.
O(log n) search space reduction on sorted arrays and monotonic solution spaces (search by answer).
Pointer manipulation, sentinel nodes, fast & slow pointers, and in-place reversal.
Binary Trees, BSTs, DFS traversals (pre/in/post), BFS level-order, and recursion tree thinking.
Bitwise XOR, AND, OR, bit masks, power of two checks, and two's complement.
Matrix rotations, spiral traversals, Euclidean GCD, prime factorizations, and geometric points.
Prefix trees for efficient string search, autocomplete, and dictionary lookups.
Min/Max heaps, top-K frequent elements, running medians, and stream processing.
DFS state-space search with choice, explore, and un-choose backtracking patterns.
Sorting by start/end times, interval merging, intersection detection, and meeting room scheduling.
Adjacency lists/matrices, BFS shortest paths, DFS cycle detection, Topological Sort, Union-Find.
Memoization, bottom-up tabulation, optimal substructure, and state transitions over 1D arrays.
Locally optimal choice paradigms, jump game reaches, gas station feasibility.
Dijkstra shortest path with min-heap, Prim/Kruskal MST, and network flow heuristics.
Grid paths, Longest Common Subsequence, 0/1 Knapsack, matrix chain multiplication, edit distance.
Arrays & Hashing
9 Problems in NeetCode 150 Core RoadmapHash maps, sets, frequency counting, and prefix arrays. The universal prerequisite for all algorithm interviews.
BTCTCI Principle: Hash Maps trade O(N) space for O(1) lookups. Look for opportunities to use Prefix Sums for O(1) range queries, and Frequency Maps to eliminate O(N^2) pairwise comparisons.