📄️ What is Time Complexity?
Time complexityis a way to describe how therunning time of an algorithm growsas theinput size increases.
📄️ Sliding Window: A Smarter Way to Loop
Welcome to one of the most useful patterns in algorithmic problem-solving: the sliding window. If you've just learned about time complexity, this is a perfect next step — because it shows you how to take a slow solution and make it blazingly fast.
📄️ Two Pointer Technique: Efficient Traversals for Scalable Code
After learning about the Sliding Window, we're ready to meet another powerful pattern that helps us write time-efficient, space-conscious, and scalable solutions: the Two Pointer Technique.
📄️ Prefix Sums: Efficient Range Summation
Prefix sums are one of the most powerful tools for problems that involve repeated sum calculations over subarrays or substrings. With a single pass of preprocessing, you can turn a brute-force O(n) sum into an instant O(1) lookup!
📄️ Hash Maps & Sets: The Power of Constant-Time Lookup
Hashing is one of the most powerful tools in a programmer’s toolbox when solving problems that require fast lookups, frequency counting, duplicate detection, or efficient pair and range queries — especially when time constraints make brute-force solutions impractical.