Welcome to data structures and algorithms! A data structure is a way to organize and store data efficiently, like this array holding numbers. An algorithm is a step-by-step procedure to solve problems, such as this sorting algorithm that arranges the numbers in order.
Let's explore common data structures. Arrays store elements in fixed-size sequential order. Linked lists use dynamic chains of connected nodes. Stacks follow Last In First Out principle, like a stack of plates. Queues follow First In First Out, like a line of people waiting. Each structure has specific use cases and performance characteristics.
Algorithms solve different types of problems. Sorting algorithms like bubble sort arrange data in order. Searching algorithms like binary search efficiently find specific elements. Graph algorithms navigate networks and relationships. Dynamic programming optimizes complex problems by breaking them down. Greedy algorithms make locally optimal choices at each step.
Algorithm efficiency is measured using Big O notation. Time complexity shows how execution time grows with input size. Space complexity measures memory usage. Constant time O(1) is fastest, linear O(n) grows proportionally, and quadratic O(n²) grows exponentially. Understanding complexity helps choose the right algorithm for each problem.
Data structures and algorithms power countless real-world applications. Web development uses hash tables for fast caching. Games employ pathfinding algorithms for character movement. Databases use B-trees for efficient indexing. AI and machine learning rely on graph structures. Operating systems use algorithms for process scheduling. Understanding these concepts is essential for building efficient software systems.