Learn Data Structure & Algorithm Pattern Wise | FAANG Ready

Explore geometric progression, where each term multiplies by a fixed ratio, and relate it to the divide and conquer rule used in binary search.

Understand log base two via a geometric progression, showing how halving n yields log2(n) iterations and the accompanying O(log n) time pattern.

Discover constant time complexity, O(1), where the number of operations stays the same regardless of input size, demonstrated by direct array access with zero-based indexing.

Compare system performance by measuring the number of iterations, not hardware speed. Debunk the myth that faster machines always reduce time complexity and thus run programs faster.

Explore how n log n time arises: perform a log n operation for each of n elements, with an outer loop of n and an inner loop halving until one.

Apply asymptotic analysis to see how algorithms scale by examining growth with input size, ignoring constants and hardware differences to reveal time and space complexity for large n.

Learn how asymptotic notations describe algorithm growth, using big O for worst-case time, big omega for lower bounds, and big theta for tight bounds across input sizes.

Define space complexity as memory usage that grows with input size, distinguishing input space from auxiliary space, and illustrate constant versus linear space with variables, arrays, and recursive call stacks.

Reverse a specific part of an array using left and right indices to selectively invert elements. This partial reversal preserves other sections and runs in O(n) time with O(1) space.

Master prefix sums to accelerate range queries in arrays, replacing brute-force sums with constant-time lookups. Grasp the start-end index rule and the prefix sum formula used in faang interview problems.

Explore the carry forward technique for solving arrays, strings, and substrings, carrying forward past information to optimize brute force solutions from left to right.

Learn to print all subarrays of an array, count them with n(n+1)/2, and explore naive O(n^3) code with nested loops, plus hints at prefix and contribution techniques for faster solutions.

Explore how prefix sums optimize subarray sums from O(n^3) to O(n^2) by building a prefix array to compute range sums efficiently.

Apply the contribution technique to count subarrays that include a given element, deriving that there are (i+1) starting points and (n-i) ending points, guiding the maximum subarray sum calculation.

Explore the sliding window technique with a fixed window size to find the subarray of size three with the maximum sum by sliding the window one index at a time.

Discover how binary search uses divide and conquer to discard half of the array and locate the target by checking the middle element.

Explore the time complexity of binary search, which halves the search space at each step using the middle element. It yields O(log n) time, via log base two.

Learn to find the first and last positions of a target in a sorted array using binary search, via two searches to achieve log n time for LeetCode problem 34.

Discover how to compute the floor square root of a nonnegative integer with binary search, without built-in sqrt, using left, right, and mid, plus edge case handling.

Compare the growth of log n and root n to understand why logarithmic time algorithms, like binary search, scale efficiently for large data sets.

Define a node class with an integer value and an optional next pointer to build a linked list, connect head to node two and tail, and print values.

Use a stack to validate strings of multiple bracket types, ensuring each closing bracket matches the correct open bracket and appears in proper order.