This visualization explains Big O notation by pairing a written tutorial with an illustrative graph of algorithmic complexity. The graph plots the number of operations against input size for common complexity classes, including O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), and O(n!). It uses a static image sourced from Big O Cheat Sheet, rendered with standard Markdown image embedding, and is accompanied by Python code examples that demonstrate each growth rate, such as `print_first` for O(1) and nested loops for O(n²). The example shows how runtime scales with input size and highlights the trade-off between time and space efficiency.