Data Structure

Data Structures Course

Course Overview

Our Data Structures course is designed to provide you with a comprehensive understanding of fundamental data structures and their applications in computer science. This course will equip you with the knowledge and skills needed to design efficient algorithms, manage data effectively, and solve complex problems in software development.

Who Should Attend?

  • Software Developers seeking to strengthen their understanding of data structures
  • Computer Science Students aiming to enhance their knowledge and skills in algorithms and data management
  • IT Professionals looking to advance their careers in software engineering and development
  • Anyone interested in learning about data structures and algorithmic problem-solving

Prerequisites

  • Basic knowledge of programming concepts (e.g., variables, loops, functions)
  • Familiarity with any programming language (e.g., C, C++, Java, Python)

Module 1: Introduction to Data Structures

  • Overview of Data Structures
    • Importance and role of data structures in programming
    • Classification of data structures: linear vs. non-linear, static vs. dynamic
  • Basic Data Structures
    • Arrays and linked lists
    • Implementation, operations, and complexity analysis

Module 2: Stacks and Queues

  • Stacks
    • Implementation of stacks using arrays and linked lists
    • Stack operations: push, pop, peek
  • Queues
    • Implementation of queues using arrays and linked lists
    • Queue operations: enqueue, dequeue, peek

Module 3: Trees

  • Binary Trees
    • Properties and representation of binary trees
    • Traversal techniques: inorder, preorder, postorder
  • Binary Search Trees (BST)
    • Operations: insertion, deletion, searching
    • Balancing BSTs: AVL trees, Red-Black trees

Module 4: Heaps and Priority Queues

  • Heaps
    • Min-heaps and max-heaps
    • Heap operations: insert, delete, heapify
  • Priority Queues
    • Implementation using heaps
    • Priority queue operations

Module 5: Hashing

  • Hash Functions and Hash Tables
    • Understanding hash functions and collision resolution techniques
    • Hash table operations: insert, search, delete
  • Applications of Hashing
    • Implementing hash maps and dictionaries
    • Solving problems using hashing techniques

Module 6: Graphs

  • Graph Representation
    • Adjacency matrix and adjacency list
    • Graph traversal: BFS (Breadth-First Search) and DFS (Depth-First Search)
  • Graph Algorithms
    • Shortest path algorithms: Dijkstra’s algorithm, Bellman-Ford algorithm
    • Minimum spanning tree algorithms: Prim’s algorithm, Kruskal’s algorithm

Module 7: Advanced Data Structures

  • Advanced Topics in Data Structures
    • Trie (Prefix Tree), Segment Tree, Fenwick Tree (Binary Indexed Tree)
    • Applications and implementation examples
  • Dynamic Programming
    • Understanding dynamic programming concepts
    • Solving optimization problems using dynamic programming

Module 8: Real-World Applications and Projects

  • Project Work
    • Applying data structures and algorithms to real-world problems
    • Designing and implementing efficient solutions
  • Case Studies
    • Analyzing and solving algorithmic challenges
    • Learning from industry-relevant examples

Course Duration

  • Total Duration: 60 hours
  • Schedule: Flexible timing options available

Course Material

  • Comprehensive course materials, including lecture notes and coding exercises
  • Access to recorded sessions for review and practice
  • Practical assignments and hands-on projects

Certification

  • Upon successful completion, participants will receive a certificate of completion