DSA Roadmap

Data structures and algorithms are how engineers think about problems: choose a representation, design a procedure, prove it correct, and predict its cost. This roadmap teaches that thinking in Go, from a first loop invariant to production structures such as LRU caches and concurrent maps, with small examples in every lesson and larger runnable demos in the code viewer.

Scope: Five phases from beginner to expert. Study the lessons in order, run every demo locally with go run file.go (Go 1.23 or newer), and complete each lesson's Practice Lab before moving on.

DSA Study Progress

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#	DSA	Topic	Description
PHASE 1: FOUNDATIONS
	01	Thinking in Algorithms	Specify, work examples, brute force first, loop invariants, and table-driven tests.
	02	Complexity Analysis	Big O, Omega, Theta, reading cost from code, amortized cost, and doubling experiments.
	03	Recursion & the Call Stack	Base case and reduction, stack frames, recurrences, and converting recursion to loops.
	04	Data Structures Map	Go arrays, slices, maps, stdlib containers, a cost catalog, and choosing a structure.
PHASE 2: CORE DATA STRUCTURES
	05	Arrays & Slices	Slice internals, two pointers, sliding window, and prefix sums.
	06	Linked Lists	Singly and doubly linked lists, sentinel nodes, reversal, and cycle detection.
	07	Stacks & Queues	Stacks, queues, ring buffers, deques, and monotonic stacks.
	08	Hash Tables	Hash functions, collisions, a map from scratch, and counting patterns.
	09	Binary Trees & BST	Traversals, recursive tree thinking, and binary search tree operations.
	10	Heaps & Priority Queues	Binary heaps, container/heap, top-k, and k-way merge.
PHASE 3: CORE ALGORITHMS
	11	Searching	Linear and binary search, lower/upper bounds, and binary search on the answer.
	12	Sorting	Elementary sorts, merge sort, quicksort, counting and radix sort, stability.
	13	Graph Traversal	Representations, BFS, DFS, topological sort, and union-find.
	14	Shortest Paths & MST	Dijkstra, Bellman-Ford, A*, and Kruskal/Prim spanning trees.
PHASE 4: ADVANCED ALGORITHMS
	15	Greedy Algorithms	Local choices, exchange arguments, intervals, and Huffman coding.
	16	Dynamic Programming	Memoization, tabulation, state design, and classic DP problems.
	17	Backtracking	Subsets, permutations, N-Queens, constraint search, and pruning.
	18	Advanced Trees	Balanced trees, tries, segment trees, and Fenwick trees.
	19	String Algorithms	KMP, rolling hash, tries in practice, and suffix arrays.
	20	Advanced Techniques	Bit manipulation, randomized algorithms, Bloom filters, and HyperLogLog.
	21	Production Structures	LRU cache, rate limiter, consistent hashing, and concurrent structures in Go.
PHASE 5: PRACTICE & REFERENCE
	22	Demo Examples	Runnable, commented Go programs for every phase, in the code viewer.
	23	Study Projects	Open-source Go projects to read and contribute to: algorithm collections, caches, storage engines, and more.
	24	References	Go documentation and playgrounds, visualizers, free and classic books, practice sites, and key papers.

This tutorial was realized with Claude Code: Sonnet 5.5.