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Analyze Complexity From Code

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Why Code Analysis Matters

**Objective**: Learn to look at a piece of code and estimate its time and space complexity quickly. By the end, you'll be able to: - Identify the input variables that drive growth. - Translate loops, nested loops, branches, recursion, and built-ins into Big O. - Separate time complexity from auxiliary space complexity. - Explain your analysis clearly in an interview.

Now that we've covered Understanding Big O Notation and Algorithmic Complexity, the Big O Growth Patterns Cheat Sheet, and Understanding Space Complexity, we're ready for the practical skill interviewers care about most:

Given code, can you tell how it scales?

This is different from memorizing that a pattern is O(n) or O(n^2). In real interviews, you'll often write a solution first, then the interviewer asks: "What's the complexity?"

You need a repeatable way to inspect the code you just wrote.

Why Code Analysis Matters

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