Master Big O Notation: Simplified Guide to Time & Space Complexity 🚀

Understanding Big O notation is essential for software engineers, especially those that are interviewing.

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00:00 - Intro
00:08 - An Analogy
00:27 - Time Complexity
01:11 - Definition
01:21 - Best Case, Worst Case, Expected Case
02:25 - Space Complexity
02:51 - Simplify
03:46 - Academia

An engineer who understands Big O notation can more accurately estimate how an algorithm will perform with larger datasets. This predictive ability is crucial in the world of big data, where algorithms must often process vast amounts of information. If an algorithm's performance degrades rapidly with increasing input size, it may not be a suitable choice for large-scale data processing. Conversely, an algorithm that scales well with input size, as indicated by its Big O notation, could be an ideal choice in such circumstances.

In addition, Big O notation helps software engineers make informed decisions about trade-offs between different algorithmic solutions. A solution with a faster running time may consume more memory, or vice versa. Understanding Big O notation helps engineers balance these trade-offs, choosing the most suitable algorithm for their specific requirements and constraints.

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