Course Outline

🔹 Day 1: Fundamental Data Structures

Session 1: Introduction to Data Structures & Algorithm Complexity

• Importance of data structures in programming
• Big O notation, time and space complexity
• Analyzing algorithm efficiency

Session 2: Arrays and Linked Lists

• Arrays: implementation, operations, and JavaScript specifics
• Singly and doubly linked lists: concepts and coding
• Common problems: reversing, detecting cycles

Session 3: Stacks and Queues

• Stack and queue concepts and use cases
• Implementing stacks and queues in JavaScript
• Applications: expression evaluation, sliding window problems

Lab Activities:

• Implement and manipulate arrays and linked lists
• Build stack and queue data structures
• Solve basic problems using these structures

🔹 Day 2: Trees, Graphs, and Recursion

Session 1: Trees and Binary Trees

• Tree terminology and traversal methods (inorder, preorder, postorder)
• Binary search trees (BST) and operations
• Balanced trees overview (AVL, Red-Black trees concept)

Session 2: Graphs and Graph Traversal

• Graph representations: adjacency list and matrix
• BFS and DFS algorithms explained and coded
• Applications of graph traversal

Session 3: Recursion and Backtracking

• Understanding recursion and call stack
• Recursive solutions for common problems
• Introduction to backtracking with examples

Lab Activities:

• Implement tree traversals and BST operations
• Write BFS and DFS for graph traversal
• Solve recursive problems and simple backtracking puzzles

🔹 Day 3: Sorting, Searching, and Algorithm Design

Session 1: Sorting Algorithms

• Bubble sort, selection sort, insertion sort
• Merge sort and quicksort: divide and conquer strategies
• Comparison and performance analysis

Session 2: Searching Algorithms

• Linear search and binary search
• Search in rotated sorted arrays and string matching basics
• Hashing and hash tables introduction

Session 3: Algorithm Design Techniques

• Greedy algorithms overview
• Dynamic programming fundamentals
• Problem-solving strategies and tips

Lab Activities:

• Implement various sorting algorithms and analyze their efficiency
• Code searching algorithms and hash table usage
• Solve algorithmic challenges using greedy and dynamic programming approaches