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Data Structure Training

30 Working Days (Daily 1.5 Hours)
★ Featured

Overview

The Data Structure Training program provides a strong foundation in organizing and managing data efficiently. Students learn how data structures and algorithms form the backbone of computer science, enabling optimal performance in software systems. Through practical examples and projects, participants gain proficiency in arrays, stacks, queues, linked lists, trees, graphs, and advanced topics such as hashing, AVL trees, and dynamic programming.

Who Can Attend

Course Content

Introduction to Data Structures and Algorithms

  • Definition and Importance of Data Structures
  • Algorithms and Their Performance Analysis
  • Time and Space Complexity
  • Asymptotic Notations: Big O, Omega, Theta

Fundamentals and Memory Management

  • Arrays, Structures, and Pointers
  • Dynamic Memory Allocation Functions: malloc(), calloc(), realloc(), free()

Stacks

  • Stack Operations: push(), pop(), peek(), display(), isEmpty(), isFull()
  • Stack Implementation using Arrays and Pointers
  • Applications: Decimal to Binary, String Reverse, Towers of Hanoi, Balanced Parentheses
  • Expression Conversion: Infix, Prefix, Postfix and their Implementations

Queues

  • Queue Operations: enqueue(), dequeue()
  • Static and Dynamic Queue Implementation
  • Circular Queues and Double-Ended Queues (Deques)
  • Applications of Queues in Real Systems

Linked Lists

  • Single Linked List: Construction, Insertion, Deletion, Sorting, Reversal, Swapping
  • Applications: Implementing Stack and Queue using Linked Lists
  • Doubly Linked List and Circular Linked List
  • Circular Doubly Linked List Concepts and Implementation

Trees and Binary Trees

  • Tree Terminology and Binary Tree Representation
  • Differences between Trees and Binary Trees
  • Expression Trees and Traversals: In-order, Pre-order, Post-order
  • Binary Search Tree (BST): Insertion, Deletion, Searching

Graphs and Traversal Algorithms

  • Graph Introduction and Terminology
  • Graph Representations: Adjacency Matrix and List
  • Graph Traversal Algorithms: BFS (Breadth First Search) and DFS (Depth First Search)

Searching and Sorting Algorithms

  • Linear and Binary Search Techniques
  • Sorting Methods: Bubble, Selection, Insertion, Heap, Merge, and Quick Sort

AVL Trees

  • Introduction and Difference between BST and AVL Trees
  • Tree Rotations: LL, RR, LR, RL
  • Insertion, Deletion, and Traversal in AVL Trees

Red-Black Trees

  • Comparison: BST vs AVL vs Red-Black Tree
  • Tree Rotations and Balancing
  • Insertion and Deletion in Red-Black Trees

B-Trees and Hashing

  • M-way Search Tree: Search, Insertion, Deletion
  • Hash Table Representation and Hash Functions (Division Method)
  • Collision and Collision Resolution Techniques: Chaining, Linear and Quadratic Probing, Double Hashing, Rehashing

Priority Queues and Heaps

  • Definition and Operations: Insertion, Deletion
  • Max Heap and Min Heap Implementation
  • Heap Construction and Applications

String Matching and Pattern Algorithms

  • Brute Force Algorithm
  • Boyer–Moore Algorithm
  • Knuth-Morris-Pratt (KMP) Algorithm

Tries and Advanced Topics

  • Standard Tries, Compressed Tries, and Suffix Tries
  • Introduction to Dynamic Programming
  • Greedy Method
  • Divide and Conquer Method