Complexity Analysis

This lecture introduces complexity analysis, emphasizing its role as the foundation of data structures and algorithms. It discusses how different solutions to a problem can vary in performance, focusing on time and space complexity. The lecture defines good code as being both readable and scalable, with scalability involving both speed and memory efficiency. Big-O notation is introduced as a tool to evaluate and rate code performance. The lecture concludes with a preview of the next session, which will delve deeper into Big-O notation and complexity analysis.

What is the significance of complexity analysis in coding interviews?

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How can you define a good program or good code?

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What are the two main factors that define a good code base?

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Explain the difference between time complexity and space complexity.

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What is big-O notation and how is it used in evaluating code?

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How do you determine if one implementation is faster than another?

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What are the different ratings used in big-O notation to define code performance?

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