Prakash 🚀👨💻 retweetledi
DATA STRUCTURES & ALGORITHMS (DSA) ROADMAP
FOUNDATIONS
What is DSA → Study of organizing data and solving problems efficiently
Why DSA → Improves problem-solving and coding interview performance
Time Complexity → Measure execution time using Big-O notation
Space Complexity → Measure memory usage
Big-O Notation → O(1), O(n), O(log n), O(n²)
Recursion Basics → Function calling itself
PROGRAMMING BASICS
Variables → Store data
Data Types → int, float, string, boolean
Control Structures → if/else, switch
Loops → for, while, do-while
Functions → Reusable blocks of code
Input/Output → Handling user data
ARRAYS
Basics → Store elements in contiguous memory
Traversal → Access elements sequentially
Insertion → Add elements at index
Deletion → Remove elements
Searching → Linear Search, Binary Search
Two Pointers → Optimize array problems
Sliding Window → Efficient subarray problems
STRINGS
String Basics → Sequence of characters
Manipulation → Concatenation, slicing
Pattern Matching → KMP Algorithm
Palindrome Problems → Reverse & compare
Anagrams → Frequency counting
String Hashing → Efficient comparisons
LINKED LISTS
Types → Singly, Doubly, Circular
Traversal → Move through nodes
Insertion → Beginning, middle, end
Deletion → Remove nodes
Reversal → Reverse linked list
Cycle Detection → Floyd’s Cycle Algorithm
STACKS
LIFO Principle → Last In First Out
Operations → push, pop, peek
Applications →
→ Expression evaluation
→ Parentheses matching
→ Undo/Redo systems
QUEUES
FIFO Principle → First In First Out
Types → Simple Queue, Circular Queue, Deque, Priority Queue
Operations → enqueue, dequeue
Applications → Scheduling, buffering
HASHING
Hash Tables → Key-value storage
Hash Functions → Map keys to indices
Collision Handling → Chaining, Open Addressing
Applications → Fast lookup, caching
TREES
Binary Tree → Hierarchical structure
Binary Search Tree (BST) → Ordered tree
Traversals →
→ Inorder → Left, Root, Right
→ Preorder → Root, Left, Right
→ Postorder → Left, Right, Root
Heap → Min Heap, Max Heap
Trie → Efficient string storage
GRAPHS
Representation → Adjacency List, Matrix
Traversal →
→ BFS (Breadth-First Search)
→ DFS (Depth-First Search)
Shortest Path → Dijkstra, Bellman-Ford
Minimum Spanning Tree → Prim’s, Kruskal’s
Topological Sorting → Directed graphs
ALGORITHMIC TECHNIQUES
Recursion → Divide problems into subproblems
Backtracking → Try all possibilities (e.g., N-Queens)
Greedy Algorithms → Local optimal choices
Divide & Conquer → Break into smaller parts (Merge Sort)
Dynamic Programming → Store results to avoid recomputation
SORTING ALGORITHMS
Bubble Sort → Simple but inefficient
Selection Sort → Select minimum element
Insertion Sort → Build sorted array
Merge Sort → Divide & conquer
Quick Sort → Partition-based sorting
Heap Sort → Use heap structure
SEARCHING ALGORITHMS
Linear Search → Check each element
Binary Search → Divide search space
Advanced Searching → Ternary search
ADVANCED TOPICS
Segment Trees → Range queries
Fenwick Tree (BIT) → Efficient updates
Disjoint Set (Union-Find) → Connected components
Bit Manipulation → Optimize computations
Matrix Algorithms → Grid-based problems
PROBLEM-SOLVING STRATEGY
Understand Problem → Clarify requirements
Brute Force → Start simple
Optimize → Improve time/space complexity
Dry Run → Test with examples
Edge Cases → Handle all scenarios
Practice → Consistency is key
PLATFORMS TO PRACTICE
LeetCode → Interview-focused problems
Codeforces → Competitive programming
HackerRank → Beginner to advanced
GeeksforGeeks → Tutorials + problems
QUICK LEARNING PATH
Learn Basics → Programming + Big-O
Master Core DS → Arrays, Strings, Linked Lists
Learn Trees & Graphs → Advanced structures
Practice Algorithms → Sorting, Searching, DP
Solve Problems Daily → Build consistency
Prepare for Interviews → Mock tests & challenges
Grab the DSA EBOOK:
codewithdhanian.gumroad.com/l/jnpmsr
English
