Big O notation describes an algorithm's runtime complexity in terms of input size's upper bound. It helps compare algorithm performance as data grows, focusing on scalability and efficiency.
Big O notation aids in algorithm efficiency comparison, behavior prediction with larger datasets, code optimization, and resource management, key for software engineering and CS development.
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