A comprehensive roadmap that teaches DSA from first principles. Every concept is learned by understanding the theory, implementing it from scratch, comparing Python and Go implementations, and solving carefully selected NeetCode problems. Instead of memorizing solutions, you'll learn to recognize patterns and build deep intuition.
Learn the Python and Go language features needed specifically for solving Data Structures and Algorithms problems.
Master the Python features, standard library modules, and language idioms used in algorithmic problem solving.
Learn Go's slices, maps, structs, pointers, standard library, and language features required for implementing algorithms efficiently.
Learn to analyze the efficiency of algorithms before writing code.
Big-O, Big-Theta, Big-Omega, amortized analysis, recursion depth, and practical performance considerations.
The foundation of almost every interview problem.
Dynamic arrays, memory layout, prefix sums, suffix sums, hashing, two pointers, and sliding window.
Learn string algorithms, Unicode, UTF-8, and common interview patterns.
String manipulation, pattern matching, hashing, encoding, and efficient string algorithms.
One of the most important data structures in modern programming.
Hash functions, collisions, chaining, probing, resizing, Python dictionaries, and Go maps.
Understanding pointers, references, and efficient node manipulation.
Singly, doubly, circular lists, reversal, cycle detection, merging, and fast/slow pointer techniques.
LIFO data structures and the problems they solve elegantly.
Monotonic stacks, expression evaluation, recursion, DFS, and stack-based interview patterns.
FIFO structures, deques, and breadth-first algorithms.
Queues, circular queues, deques, priority queues, and BFS applications.
Much more than searching sorted arrays.
Binary search templates, searching on answers, predicates, rotated arrays, and optimization problems.
Recursive thinking and hierarchical data structures.
Binary trees, BSTs, traversals, tries, heaps, segment trees, Fenwick trees, and balanced trees.
Priority queues and efficient top-k processing.
Min heaps, max heaps, heap construction, scheduling problems, and top-k algorithms.
Representing relationships and traversing complex structures.
DFS, BFS, shortest paths, topological sorting, MSTs, Union-Find, and graph algorithms.
Master recursive thinking before Dynamic Programming.
Recursion trees, decision trees, backtracking, memoization foundations, and recursion internals.
Build intuition for solving optimization problems using overlapping subproblems and optimal substructure.
Memoization, tabulation, 1D DP, 2D DP, LIS, LCS, Knapsack, Tree DP, Bitmask DP, and problem-solving intuition.
When making the locally optimal choice leads to a globally optimal solution.
Greedy strategy, interval scheduling, Huffman coding, activity selection, jump game, and exchange arguments.
Understand binary representations and solve problems elegantly using bitwise operations.
Binary numbers, bit masks, shifts, XOR tricks, subset generation, and low-level optimization.
Specialized data structures used in advanced algorithmic problems.
Union-Find, Bloom Filters, Skip Lists, Suffix Arrays, Suffix Trees, Rolling Hashes, and Persistent Data Structures.
Recognize problem-solving patterns instead of memorizing individual solutions.
Two pointers, sliding window, fast & slow pointers, merge intervals, DFS, BFS, binary search, heaps, backtracking, and dynamic programming patterns.
A complete revision and mastery phase where every NeetCode 150 problem is solved independently and reviewed.
Solve the complete NeetCode 150 independently, revisit patterns, optimize solutions, and prepare for coding interviews.
End of roadmap
You've covered all 20 topics in the Data Structures & Algorithms roadmap.