Introduction

Data structures and algorithms are fundamental to computer science and crucial for technical interviews.

Time Complexity

Understanding Big O notation is essential for analyzing algorithm efficiency.

• O(1) - Constant time
• O(log n) - Logarithmic time
• O(n) - Linear time
• O(n log n) - Linearithmic time
• O(n²) - Quadratic time
• O(2ⁿ) - Exponential time
• O(n!) - Factorial time

Common Data Structures

• Arrays and Lists
• Stacks and Queues
• Linked Lists
• Trees and Graphs
• Hash Tables
• Heaps

Sorting Algorithms

• Bubble Sort - O(n²)
• Merge Sort - O(n log n)
• Quick Sort - O(n log n) average
• Heap Sort - O(n log n)