Learn to recognize when algorithm efficiency matters and when simple is better
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Big O thinking helps you predict what breaks at scale before building.
Think about: Apps, websites, processes, or systems you use. Which would struggle with 10x or 100x more data?
Educational content slides
Big O describes how time grows as data grows:
The notation ignores constants. It's about growth patterns, not exact times.
Test your understanding with this quiz.
You have two algorithms: Algorithm A takes 100 operations for 10 items (O(N²)), Algorithm B takes 50 operations for 10 items (O(N log N)). Which performs better for 1,000 items?