Big-O: How Code Slows as Data Grows
summary
Big‑O explains how code slows as data grows and shows concrete tradeoffs using Python lists, dicts and sets and hash functions. Examples: counting is O(n) while len or dict lookup is O(1); an algorithm that becomes O(n^2) turns 2,000 points into four million operations and slows from about 0.5s to 20s; building a 50,000-element set runs ~10ms normally but collisions make it ~34s, which motivates hash randomization.
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chapters (19)
- 0:00Intro
- 1:22Mindsets
- 3:57Terminology
- 4:54Counting beans
- 6:56Finding words
- 7:59Other terms
- 9:19Determining Big-o
- 12:32An Example
- 14:57Another example
- 17:23The Graph
- 18:01Python complexities
- 19:13Trade-offs
- 22:13More possibilities
- 24:12Small numbers
- 25:27Advanced: Amortization
- 26:08Advanced: Worst case
- 27:12Advanced more math
- 27:36Experts
- 28:30Summary
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