Time complexity describes how code runtime changes with varying input sizes, crucial for assessing algorithm efficiency. Space complexity measures the amount of memory an algorithm uses based on input size. Both metrics help in writing efficient code necessary for coding interviews and improving program performance. Gaining proficiency in calculating time and space complexity aids beginners in navigating DSA challenges and developing effective coding solutions. This understanding is fundamental for clean and fast programming, leading to better algorithm design in various scenarios.
Time complexity indicates how the runtime of a piece of code increases as input size grows. It is essential for understanding algorithm efficiency.
Understanding space complexity is crucial as it measures the amount of memory your algorithm uses relative to the input size.
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