Big Oh Analysis

Data Skeptic

How long an algorithm takes to run depends on many factors including implementation details and hardware. However, the formal analysis of algorithms focuses on how they will perform in the worst case as the input size grows. We refer to an algorithm's runtime as it's "O" which is a function of it's input size "n". For example, $O(n)$ represents a linear algorithm - one that takes roughly twice as long to run if you double the input size. In this episode we discuss a few everyday examples of algorithmic analysis including sorting, search a shuffled deck of cards, and verifying if a grocery list was successfully comple

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