Member-only story
Why it becomes important to learn Time and Space Complexity? (DSA Part — 2)
How I Mastered the Basics of “Algorithm Efficiency” with Real-Life Examples.
Discover how Big-O notation helps measure efficiency with simple, real-world examples and practical coding scenarios.
My articles are open for everyone; non-member readers can read the article by clicking this link.
Other DSA Links: DSA Part — 0 | DSA Part — 1 | DSA Part — 2 | DSA Part — 2.1 | DSA Part 2.2 | DSA Part 3 | DSA Part 4 | DSA Part 5 | DSA Part — 6
“When writing code, it’s not just about making it work — it’s about making it work efficiently.”
Time and space complexity are the metrics that help you evaluate how your code performs. Whether you’re solving problems, optimizing your application, or preparing for interviews, understanding these concepts is essential.
1 — What is Time Complexity?
Time complexity measures how the runtime of an algorithm changes as the size of the input increases. It helps you predict how your code will perform for larger datasets.
