Data Structures & Algorithms
DSA Part-1
- Introduction to Data Structures
- Asymptotic Notations: Big-O, Omega, and Theta Notations
- Time Complexity and Space Complexity Analysis of Algorithms
- 1D Arrays: Memory Representation and Address Calculation
- 2D Arrays: Row-Major and Column-Major Order Formulas and Numericals
- Array Operations: Traversal, Insertion, and Deletion with Algorithms
- Sparse Matrix: Triplet Representation and Linked Representation
- String Operations, Memory Storage, and Pattern Matching Basics
- Linear Search Algorithm and Complexity Analysis
- Binary Search Algorithm (Iterative and Recursive)
- Bubble Sort Algorithm and Optimization
- Selection Sort Algorithm and Step-by-Step Trace
- Insertion Sort Algorithm and In-Place Analysis
- Merge Sort Algorithm (Divide and Conquer Approach)
- Quick Sort Algorithm and Partitioning Methods
- Counting Sort Algorithm (Non-Comparison Sorting)
- Radix Sort Algorithm and Bucket Distribution
DSA Part-2
- Singly Linked List: Concept and Memory Allocation
- Singly Linked List: Insertion Operations
- Singly Linked List: Deletion Operations
- Singly Linked List: Reversal and Search Operations
- Doubly Linked List: Node Structure and Traversal
- Doubly Linked List: Insertion and Deletion Operations
- Circular Singly Linked List: Concept and Operations
- Circular Doubly Linked List: Concept and Operations
- Header Linked List: Grounded and Circular Header Lists
- Polynomial Representation and Addition using Linked List
DSA Part-3
- Stack Data Structure: Array Implementation
- Stack Implementation using Linked List
- Infix, Prefix, and Postfix Notations
- Infix to Postfix Conversion using Stack
- Infix to Prefix Conversion using Stack
- Evaluation of Postfix Expression using Stack
- Recursion and System Stack (Tower of Hanoi)
- Linear Queue: Array Implementation and Operations
- Drawbacks of Linear Queue and Memory Wastage
- Circular Queue: Array Implementation and Condition Checks
- Queue Implementation using Linked List
- Double-Ended Queue (Deque): Input and Output-Restricted
- Priority Queue: Concept, Array and Heap Representation
DSA Part-4
- Tree Data Structure: Terminology and Basic Concepts
- Binary Trees: Types and Properties
- Binary Tree Traversals: Inorder, Preorder, and Postorder
- Level Order Traversal of Binary Tree
- Binary Search Tree (BST): Concept and Search Operation
- Binary Search Tree (BST): Insertion and Deletion Operations
- AVL Tree: Concept and Balance Factor
- AVL Tree Rotations: LL, RR, LR, and RL Rotations
- Binary Heap: Min-Heap and Max-Heap Properties
- Heap Operations: Heapify, Insertion, and Deletion
- B-Tree: Properties, Structure, and Insertion
- B+ Tree: Structure and Comparison with B-Tree
DSA Part-5
- Graph Data Structure: Terminology and Types
- Graph Representation: Adjacency Matrix and Adjacency List
- Breadth First Search (BFS) Algorithm for Graphs
- Depth First Search (DFS) Algorithm for Graphs
- Hashing: Hash Tables and Hash Functions
- Collision Resolution: Open Addressing Techniques
- Collision Resolution: Separate Chaining Method
Tree Data Structure: Terminology and Basic Concepts
Tree ek non-linear, hierarchical data structure hota hai jisme data elements nodes ke roop me arranged hote hain aur unke beech parent-child relationships hoti hain. Linear data structures (array, linked list) me elements sequential hote hain, jabki trees hierarchical information (jaise file directories, organizational charts, XML/HTML DOM trees) ko model karte hain.
Core Tree Terminology
- Root Node: Tree ka sabse top-most node jiska koi parent nahi hota (Tree me exactly 1 root hoti hai).
- Edge: Do nodes ke beech ki connecting link (directional pointer). N nodes ke tree me exactly
N - 1edges hoti hain. - Parent & Child: Agar node A se edge node B tak ja rahi hai, toh A parent hai aur B child hai.
- Siblings: Same parent ke children nodes siblings kehlate hain.
- Leaf Node (Terminal Node / External Node): Aise nodes jinka koi child nahi hota (Degree = 0).
- Degree of a Node: Us node ke total number of children.
- Degree of a Tree: Tree ke sabhi nodes me se maximum degree.
- Depth of a Node: Root node se lekar us node tak path me number of edges. (Root ki depth = 0).
- Height of a Node: Us node se lekar kisi deepest leaf node tak longest path me number of edges.
- Height of a Tree: Root node ki height (ya tree ka maximum level).
Level 0 (Root) [ A ]
/ \
Level 1 [ B ] [ C ]
/ \ \
Level 2 [ D ] [ E ] [ F ]
Nodes: A, B, C, D, E, F (Total = 6)
Edges: 5 (N - 1)
Leaves: D, E, F (Degree = 0)
Internal Nodes: A, B, C
Height of Tree: 2
Agli post me hum cover karenge Binary Trees: Types and Properties.