Understanding how quickly and how much memory an algorithm needs is super important in coding. This helps us figure out if an algorithm is actually efficient.

Time Complexity

• What It Means: Time complexity tells us how the time it takes to finish an algorithm changes as we give it more data.
• Example: Think about bubble sort. If it takes time that grows like (n^2) (where (n) is the amount of data), it will get really slow when there’s a lot of data to sort.

Space Complexity

• What It Means: Space complexity helps us understand how much memory an algorithm needs as the data size increases.
• Example: If an algorithm needs an extra list that is the same size as the data (n), then it has (O(n)) space complexity.

Why Both Are Important

• Finding the right balance between time and space is key to getting the best results. Sometimes, a really fast algorithm might use too much memory, which can cause problems when working with large amounts of data. By understanding both time and space complexity, we can make our code better!