Data Structures & Algorithms Roadmap

A beginner-to-job-ready roadmap covering core DS&A concepts, problem-solving patterns, and interview preparation for software engineering roles.

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1. Stage 1: Programming Foundations

Choose a Language (Python or Java)
A solid language base is required before tackling algorithms.
docPython Official Docs coursefreeCodeCamp – Scientific Computing with Python
Time & Space Complexity (Big-O)
Measuring efficiency is the foundation of algorithmic thinking.
videoBig O Notation – freeCodeCamp YouTube tutorialBig-O Cheat Sheet
Recursion Basics
Recursion underpins trees, graphs, and divide-and-conquer algorithms.
tutorialRecursion – Khan Academy videoRecursion in 6 Minutes – Computerphile

Arrays & Strings
Arrays are the most fundamental structure used in almost every problem.
docPython Lists – Official Docs tutorialArray Data Structure – GeeksforGeeks
Linked Lists
Linked lists teach pointer manipulation critical for interviews.
tutorialLinked List – GeeksforGeeks videoLinked Lists – CS50 YouTube
Stacks & Queues
Enable solving bracket matching, BFS, and scheduling problems.
tutorialStack Data Structure – GeeksforGeeks doccollections.deque – Python Docs
Hash Tables
O(1) lookups make hash maps essential for optimising many problems.
tutorialHash Table – GeeksforGeeks docPython dict – Official Docs

Trees & Binary Search Trees
Trees appear in databases, file systems, and countless interview questions.
tutorialBinary Tree – GeeksforGeeks videoBinary Search Tree – CS Dojo
Heaps & Priority Queues
Heaps enable efficient scheduling, top-K, and graph algorithm problems.
tutorialHeap Data Structure – GeeksforGeeks docheapq – Python Official Docs
Graphs (Representations)
Graphs model real-world networks; required for senior-level problem solving.
tutorialGraph Data Structure – GeeksforGeeks videoGraphs – freeCodeCamp YouTube
Tries
Tries specialise in fast prefix and autocomplete operations.
tutorialTrie Data Structure – GeeksforGeeks

Sorting Algorithms
Sorting is foundational; understanding trade-offs sharpens algorithmic thinking.
tutorialSorting Algorithms – GeeksforGeeks videoSorting Algorithms Visualised – Timo Bingmann
Binary Search
Reduces O(n) searches to O(log n); appears in many interview variants.
tutorialBinary Search – GeeksforGeeks tutorialBinary Search – LeetCode Explore
BFS & DFS
Graph traversals are the backbone of pathfinding and connected-component problems.
tutorialBFS and DFS – GeeksforGeeks videoBFS & DFS – freeCodeCamp YouTube
Greedy Algorithms
Greedy strategies solve optimisation problems with simple local choices.
tutorialGreedy Algorithms – GeeksforGeeks

Dynamic Programming
DP unlocks optimal substructure problems common in top-tier interviews.
videoDynamic Programming – freeCodeCamp YouTube tutorialDP Patterns – LeetCode Discuss
Sliding Window & Two Pointers
These patterns cut brute-force O(n²) solutions down to O(n).
tutorialSliding Window – GeeksforGeeks tutorialTwo Pointers – LeetCode Explore
Backtracking
Backtracking solves combinatorial problems like permutations and N-Queens.
tutorialBacktracking – GeeksforGeeks videoBacktracking – NeetCode YouTube
Dijkstra's & Bellman-Ford
Shortest-path algorithms are required for graph-heavy interview rounds.
tutorialDijkstra's Algorithm – GeeksforGeeks

Structured Problem-Solving Framework
A repeatable approach reduces panic and improves interview performance.
tutorialHow to Approach a Problem – LeetCode Explore videoHow to Solve Any Coding Problem – freeCodeCamp YouTube
LeetCode Curated Problem Sets
Deliberate targeted practice on top patterns is the fastest path to readiness.
tutorialBlind 75 List – NeetCode tutorialLeetCode Top Interview Questions
Mock Interviews
Simulated pressure exposes gaps and builds real interview confidence.
tutorialPramp – Free Peer Mock Interviews tutorialinterviewing.io – Practice with Engineers
System Design Basics (DS perspective)
Senior roles require knowing which data structures power scalable systems.
tutorialSystem Design Primer – GitHub videoSystem Design Introduction – Gaurav Sen

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