Data Structure and Algorithm with Java Script

Course Description:

This course is designed to help students master Data Structures and Algorithms (DSA) using JavaScript, a powerful and widely-used programming language. Through this course, students will gain a deep understanding of key data structures such as arrays, linked lists, stacks, and queues, and algorithms for sorting, searching, and optimization problems. They will learn how to approach problems efficiently, writing clean and optimized code.

Student Benefits:

• Enhanced Problem-Solving Skills: Mastering DSA enables students to break down complex problems and implement optimal solutions.
• Improved Coding Efficiency: Learn how to write more efficient and clean code by leveraging the right data structures and algorithms.
• Career Growth: Having a strong understanding of DSA is essential for job interviews in top tech companies and excelling in coding competitions.
• Real-World Applications: Get hands-on experience solving practical problems, preparing for industry scenarios like handling large-scale data.
• Project Development: Build end-to-end projects implementing various algorithms and structures.
• Job Readiness: The course prepares students for tech interviews, offering mock coding interview sessions.
• Support & Mentorship: Access to experienced instructors, forums, and peer support.

Course Modules:

1. Introduction to Data Structures & Algorithms
   • Why DSA is important
   • Time complexity and Big-O notation
   • Overview of algorithmic strategies
2. Arrays and Strings
   • Basic array manipulation
   • String algorithms
   • Two-pointer technique
3. Linked Lists
   • Singly and doubly linked lists
   • Operations: insertion, deletion, reversal
   • Real-world applications
4. Stacks and Queues
   • Implementation using arrays and linked lists
   • Applications in recursion, browser history, etc.
   • Solving problems using stack/queue
5. Recursion and Backtracking
   • Understanding recursion
   • Backtracking problems like N-Queens, permutations
6. Hashing and Hash Tables
   • Hash functions, collision resolution techniques
   • Hash map applications in real-time systems
7. Sorting Algorithms
   • Bubble, Selection, Insertion sort
   • Merge sort, Quick sort
   • Comparative analysis
8. Binary Search
   • Binary search algorithm
   • Search in rotated sorted arrays
   • Applications in competitive programming
9. Trees and Binary Search Trees (BST)
   • Tree traversal techniques (DFS, BFS)
   • Balanced trees (AVL, Red-Black)
   • Problems on binary trees and BST
10. Graphs and Graph Algorithms
    • Graph representation and traversal (BFS, DFS)
    • Shortest path algorithms (Dijkstra, Bellman-Ford)
    • Topological sorting, cycle detection
11. Dynamic Programming
    • Concepts of memorization and tabulation
    • Classic DP problems like knapsack, longest common subsequence
12. Greedy Algorithms
    • Greedy approach to problem-solving
    • Huffman coding, interval scheduling
13. Advanced Data Structures
    • Heaps and Priority Queues
    • Tries for prefix search
    • Segment trees, Fenwick trees