Understanding complex ideas can be tricky, especially when it comes to algorithms. Visualizing these ideas with graphs and charts helps us see how well they work as the input size changes. Let's take a look at a simple example: sorting things.

1. Time Complexity: When we look at how many steps an algorithm takes as we give it more data, we can see a big difference. For instance, bubble sort is slower because it needs many more steps as the list gets bigger. We can show this growth as $O(n^2)$. On the other hand, quicksort is faster and needs fewer steps, which we show as $O(n \log n)$. This means that quicksort is a better choice when we want things done quickly.

2. Space Complexity: It's also important to see how much memory (or space) an algorithm needs. By visualizing this, we can find algorithms that use less memory, making them more efficient.

In summary, these visuals, like graphs and charts, turn complicated ideas into something we can easily understand. They help us compare how well different algorithms work as we learn to code!