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StartIntroduction to DSA
Foundations, complexity, and getting interview-ready
  • 🔍 What is DSA?
  • 🎯 Why Learn DSA?
  • 📖 Key Terminology (Time/Space Complexity & Notations)
  • 📈 Time Complexity Growth Chart (ASCII Visual)
  • 🛠 Worked Example — Find Maximum Element
  • 💬 Interview Q&A
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MathMathematics for DSA
Number theory, recurrences, combinatorics, and bit tricks
  • 🔁 Recursion Basics
  • 🔄 Recurrence Relations
  • ➗ Modular Arithmetic
  • 🔍 Prime Numbers — Sieve of Eratosthenes
  • 🤝 GCD & LCM — Euclidean Algorithm
  • ⚡ Fast Exponentiation (Binary Power)
  • 🔑 Modular Multiplicative Inverse
  • 🐇 Fibonacci in O(log n) — Matrix Power
  • 🧩 Bit Manipulation Tricks
  • 📐 Combinatorics — nCr % m
  • 💬 Interview Q&A
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CoreArrays
Patterns: prefix sum, sliding window, two pointers
  • 📌 What is an Array?
  • 🧩 Characteristics
  • ⚙️ Basic Operations & Complexities
  • 🔍 Linear Search
  • 📈 Binary Search
  • ➕ Prefix Sum
  • ➖ Difference Array
  • 🚪 Sliding Window
  • 🎯 Two Pointers
  • 💵 Kadane’s Algorithm
  • 🔄 Rotate Array (Reversal)
  • 🔍 Binary Search Variants
  • 🌊 Trapping Rain Water
  • 📅 Merge Intervals
  • 🗳 Majority Element (Boyer–Moore)
  • 🎨 Dutch National Flag
  • 🔢 Count Subarrays with Sum = k
  • ✖️ Product of Array Except Self
  • 🪟 Sliding Window Maximum (Deque)
  • 🔄 Search in Rotated Sorted Array
  • 📏 lower_bound / upper_bound
  • 🧮 2D Arrays (Matrix Ops)
  • 🕵 Find Duplicates (In-place)
  • ❓ Missing Number (XOR)
  • 0️⃣ Set Matrix Zeroes (O(1) Space)
  • ⏭ Next Greater Element (Monotonic Stack)
  • 📏 Longest Consecutive Sequence (Hash)
  • 🏔 Peak Element
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CoreStrings
From brute-force search to advanced linear-time algorithms
  • 🔄 Reverse a String
  • 🔍 Palindrome Check
  • 🔠 Anagram Check
  • 🪟 Naive Substring Search
  • 🧮 KMP Algorithm
  • 🔢 Rabin–Karp (Rolling Hash)
  • 🧾 Z-Algorithm
  • 🕵 Boyer–Moore (Bad Character)
  • 🔄 Longest Palindromic Substring — Expand Center
  • ⚡ Manacher’s Algorithm
  • 📏 Minimum Window Substring
  • 📝 Edit Distance (Levenshtein)
  • 📚 Longest Common Subsequence (LCS)
  • 🪄 Word Break (DP)
  • 🌳 Trie (Prefix Tree)
  • 🔍 Aho–Corasick (Multi-pattern)
  • 🔗 Rolling Hash + O(1) Substring Hash
  • 📜 Suffix Array
  • 🤖 Suffix Automaton
  • 🧰 String Utilities (Rotation, Group Anagrams, LCP)
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CoreSearching & Sorting
Binary search patterns and classic sorting algorithms
  • 🔍 Searching
  • 1.1 Linear Search
  • 1.2 Binary Search
  • 1.3 Ternary Search
  • 1.4 Binary Search on Answer
  • 🗂 Sorting
  • 2.1 Bubble Sort
  • 2.2 Selection Sort
  • 2.3 Insertion Sort
  • 2.4 Merge Sort
  • 2.5 Quick Sort
  • 2.6 Heap Sort
  • 2.7 Counting Sort
  • 🎯 Example Problem — Square Root via Binary Search
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CoreRecursion & Backtracking
Templates, call stack intuition, and famous puzzles
  • 🔄 Recursion
  • 1.1 Definition & Rules
  • 1.2 Why Use Recursion
  • 1.3 How It Works (Call Stack)
  • 1.4 Example: Factorial
  • 1.5 Common Recursive Problems
  • 🔙 Backtracking
  • 2.1 Definition
  • 2.2 Why Use Backtracking
  • 2.3 How It Works
  • 2.4 Backtracking vs Brute Force
  • 🧮 Key Examples
  • 3.1 Generate All Subsets
  • 3.2 N-Queens
  • 3.3 Sudoku Solver
  • 3.4 Rat in a Maze
  • 3.5 Word Search
  • 📦 Backtracking Template
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CoreLinked List
From basics to LRU cache and complex operations
  • 🔍 What is a Linked List?
  • 🧩 Types of Linked Lists
  • 📊 Advantages & Disadvantages
  • ⚙️ Basic Operations & Complexities
  • 🧱 Node Definitions & Utilities
  • 🔧 Core Singly Linked List Operations
  • 🔄 Reversal (Iterative & Recursive)
  • 🐢 Middle & K-th from End
  • 🔁 Cycle Detection & Start Node
  • ➕ Merge Two Sorted Lists & Merge Sort
  • 🔀 Reverse in k-Group
  • 🔄 Palindrome Linked List
  • ➕ Add Two Numbers
  • 🔗 Intersection of Two Lists
  • ❌ Remove N-th from End
  • ↔ Reorder List
  • 📝 Copy List with Random Pointer
  • ↔ Doubly Linked List Basics
  • 🗂 LRU Cache Design
  • 🔄 Circular Linked List Basics
  • 📚 Flatten Multilevel DLL
  • 🎯 Typical Interview Patterns
  • 🖼 ASCII Visuals & Cheat Hints
  • 💼 Rapid-Fire Interview Q&A
  • 🏋 Practice Problems
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CoreStack
LIFO, expression evaluation, and monotonic patterns
  • 📖 Definition & LIFO Concept
  • ⚙️ Core Operations & Time Complexity
  • 🛠 Implementation
  • 3.1 Array-based Stack
  • 3.2 Linked List-based Stack
  • 🎨 Visualization
  • 🌟 Famous Stack Problems
  • 5.1 Balanced Parentheses 🧮
  • 5.2 Next Greater Element 🔍
  • 5.3 Postfix Evaluation 🧾
  • 5.4 Largest Rectangle in Histogram 📊
  • 5.5 Celebrity Problem 🎉
  • 5.6 DFS (Iterative) 🌳
  • 5.7 Undo/Redo ✏️
  • 📋 Quick Reference Table
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CoreQueues
FIFO, circular queues, deque, and BFS patterns
  • 📖 Definition & FIFO Concept
  • ⚙️ Core Operations & Why Front/Rear
  • 🗂 Types of Queues
  • 3.1 Simple Queue
  • 3.2 Circular Queue
  • 3.3 Double-Ended Queue (Deque)
  • 3.4 Priority Queue
  • ⏱ Time Complexity Table
  • 🎨 Visualizations
  • 🛠 Implementations
  • 6.1 Array-based Queue
  • 6.2 Deque (collections.deque)
  • 6.3 Priority Queue (heapq)
  • 🌟 Famous Queue-Based Algorithms
  • A. BFS 🌳
  • B. Rotten Oranges 🍊
  • C. Sliding Window Maximum 🪟
  • D. LRU Cache 🧠
  • 📋 Quick Reference Table
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CoreTree
Traversals, BSTs, and advanced trees
  • 🌱 Tree Basics
  • 🌿 Binary Tree & Types
  • 🛤 Tree Traversals (DFS & BFS)
  • ⏱ Time Complexity
  • 🧮 Example: Height of Tree
  • 💼 Level Order with Queue
  • 🌳 Binary Search Tree (BST)
  • 📈 Advanced Trees (Segment, Fenwick, Trie)
  • 🏆 Famous Tree Algorithms
  • 9.1 Lowest Common Ancestor (LCA)
  • 9.2 Diameter of Binary Tree
  • 9.3 Morris Traversal
  • 9.4 Huffman Coding Tree
  • 9.5 Serialize & Deserialize
  • 9.6 Validate BST
  • 📋 Quick Reference Table
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CoreHeap
Heap operations and top-k patterns
  • 📘 Heap Basics
  • 🏗 Key Characteristics & Array Representation
  • ⏱ Time Complexity Table
  • 👀 Visual Example
  • ⚙ Heap Operations
  • 5.1 Insertion (Bubble Up)
  • 5.2 Deletion (Bubble Down)
  • 5.3 Heapify / Build Heap
  • 🎯 Applications of Heaps
  • 🏆 Famous Heap Problems
  • A. Heap Sort
  • B. Kth Largest Element
  • C. Merge K Sorted Arrays
  • D. Median of Data Stream
  • E. Minimum Cost to Connect Ropes
  • 📊 Quick Complexity Table
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CoreGraph
Traversal, shortest paths, and MST
  • 📘 Graph Basics
  • 🗂 Types of Graphs
  • 🏗 Graph Representation
  • 🔍 Graph Traversals
  • 4.1 BFS (Breadth-First Search)
  • 4.2 DFS (Depth-First Search)
  • 🛣 Shortest Path Algorithms
  • 5.1 Dijkstra’s Algorithm
  • 5.2 Bellman–Ford
  • 5.3 Floyd–Warshall
  • 🌳 Minimum Spanning Tree (MST)
  • 6.1 Kruskal’s Algorithm
  • 6.2 Prim’s Algorithm
  • 🔄 Cycle Detection
  • 🔗 Disjoint Set Union (DSU)
  • 🧪 Example Problem – Cycle Detection in Undirected Graph
  • 💡 Interview Tip – BFS vs Dijkstra
  • 📊 Quick Complexity Table
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CoreDynamic Programming
Patterns, space optimization, and famous problems
  • 📘 DP Basics
  • 🗝 Key Properties
  • 🔄 Approaches
  • 3.1 Top-Down (Memoization)
  • 3.2 Bottom-Up (Tabulation)
  • ⏱ Complexity Overview
  • 📂 DP Categories & Patterns
  • 5.1 1D DP
  • 5.2 2D DP
  • 5.3 Knapsack Variants
  • 5.4 Sequence DP
  • 5.5 Advanced DP
  • 💾 Space Optimization
  • 🌟 Famous Interview Problems by Type
  • 💡 Interview Tips
  • 📊 Quick Complexity Table
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CoreGreedy Algorithms
Greedy correctness and classic problems
  • 📘 Definition & Core Idea
  • 🛠 Properties for Greedy to Work
  • ⏳ Time Complexity Overview
  • 🎯 Visual Example – Activity Selection
  • 📋 Common Greedy Problems
  • 💻 Code Implementations
  • 6.1 Activity Selection
  • 6.2 Fractional Knapsack
  • 6.3 Job Sequencing with Deadlines
  • 6.4 Minimum Platforms for Trains
  • 💡 Interview Tip – Greedy vs DP
  • 📊 Quick Complexity Table
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CoreBit Manipulation
Operator mastery and classic bit tricks
  • 📘 Definition & Why Use It
  • 🛠 Common Bitwise Operators
  • 💡 Useful Bit Tricks
  • 👁 Visual Example
  • 💻 Example Problems & Code
  • 5.1 Count Set Bits – Kernighan’s Algorithm
  • 5.2 Swap Two Numbers Without Temp
  • 5.3 Check Power of 2
  • 5.4 Single Number (All Others Twice)
  • 5.5 Reverse Bits
  • 5.6 Generate All Subsets Using Bitmask
  • 🏆 Famous Problems by Difficulty
  • 📊 Quick Complexity Table
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CoreGame Theory
Nim Game, Grundy Numbers, and Sprague-Grundy Theorem
  • 📘 Definition & Importance
  • 🎭 Core Concepts
  • 2.1 Impartial vs Partisan Games
  • 2.2 Zero-Sum Games
  • 2.3 Optimal Play
  • 🔢 Grundy Numbers & Nimbers
  • 🏆 Famous Game Theory Problems
  • 💻 Classic Nim Game – Code Example
  • ♟ Misère Nim Strategy
  • 🧮 Grundy Number Computation
  • 🪙 Subtraction Game Example
  • 📊 Quick Complexity Table
  • 💡 Interview Tips
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