<ul data-eligibleForWebStory="false"><li>Exploring time and space complexity in algorithms before diving into coding.</li><li>Measurement using Big-O notation helps ensure optimal and efficient algorithm performance.</li><li>Understanding worst-case scenarios helps determine scalability and efficiency of algorithms.</li><li>Various Big-O notations like O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), O(n!) explained.</li><li>Implications of different time complexities on algorithm performance and the value of DSA.</li></ul>

A travel between space and time... complexity

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