Sorting and searching algorithms are fundamental techniques for organising and finding data. A sorting algorithm takes a collection of items and arranges them in a specific order — alphabetical, numerical, by size, or by any other criteria you define. A searching algorithm looks through a collection to find a specific item or determine if it exists.

While modern languages provide built in sort and search functions that handle most everyday needs, understanding the algorithms behind them makes you a significantly better programmer. You start to understand why some approaches are faster than others, and you gain the ability to write custom sorting logic for complex situations the built in tools can't handle alone.

The two most important concepts to understand early are Big O notation and algorithm efficiency. Big O describes how an algorithm's performance scales as the data grows. The most common ones you'll encounter are O(n) which means performance scales linearly with data size, O(n²) which slows down dramatically with larger datasets, and O(log n) which is very fast even on large datasets. A poor algorithm choice on a small dataset might be unnoticeable but on thousands of items it can bring your program to a halt.