Learn Data Structure & Algorithm Pattern Wise | FAANG Ready

Explore how iterations drive time complexity, from simple for loops to nested loops and recursion, and learn to count iterations to understand runtime.

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.

Provide a quick, ordered comparison of common time complexities from best to worst, covering constant time, log n, O(n), O(n log n), O(n^2), O(n^3), O(2^n), and O(n!).

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.

Understand arrays as collections of the same data type stored in contiguous memory, accessed via zero-based indexes, with Java and Swift examples, and their memory locality versus non-contiguous linked lists.

Apply a two-pointer approach to reverse an array in place by swapping left and right elements while left < right, achieving O(n) time and O(1) extra space.

Reverse an array by swapping elements from the left and right ends toward the center in a single pass, with no new array and O(n) time, O(1) space.

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.

Rotate an array by k times using three reversals: reverse the complete array, reverse 0 to k-1, then k to n-1, and apply modulo for k > n.

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 brute force range sums that loop from L to R, which becomes slow with many queries. Use prefix sum array to precompute cumulative sums and answer range queries efficiently.

Build a prefix sum array to store the cumulative total up to each index, enabling quick range sums by subtracting prefix values and reducing repeated additions.

learn to compute the even index and odd index prefix sums by constructing separate prefix arrays for an input array, updating sums at even indices and skipping odd ones.

Explore LeetCode 1664: ways to make a fair array, counting indices whose removal yields equal sums of even and odd indices, using prefix sums and even/odd balances.

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

Demonstrates moving from brute force to an optimized pair counting approach using a reverse loop with two pointers, counting g occurrences and adding g to pairs when encountering a.

Identify subarrays as continuous parts of an array, illustrated by sequences like 1-2-3-4-5, while non-contiguous segments fail. Learn sliding window, carry forward, and prefix sum techniques for analysis.

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 computing the sum of all subarrays from a brute-force O(n^3) approach and optimize with the contribution technique and prefix sums.

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.

Learn the contribution technique to optimize subarray problems from O(n^3) to O(n) time with constant space, using intuition and mathematical insight.

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.

Apply the sliding window technique to compute the maximum subarray sum of size k in an array, handling cases where k exceeds length with O(n) time and O(1) space.

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

Explore linear search by scanning an array from start to end to find a target. See binary search as a divide-and-conquer approach that halves the search space via middle-element comparisons.

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 time complexities of binary search, O(log n), and sqrt(n); show that log n grows much slower than sqrt(n) as n increases, with concrete examples.

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

Learn how a class acts as a blueprint for objects, defining properties and methods, with an initializer and concrete objects like a car.

Explore how a program stores data in memory by contrasting the stack and the heap, and how objects from classes live on the heap while references reside on the stack.

Explore the difference between deepcopy and shallow copy for reference types by contrasting shared addresses in the stack with true replicas, using a car color example.

Explore how a linked list stores a value and the address to the next node, and begin coding the first linked list with nodes, head, and tail.

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.

Traverse a linked list using a temp node created as a shallow copy of head, using the next pointer to print each node's value until the temp node is null.

Explore the stack data structure, its last-in-first-out behavior, and core operations—push, peek, pop, and is empty—along with array-based implementations and constant time updates.

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

Explore queues as first-in, first-out data structures, contrasting them with stacks and illustrating insertion and removal using a ticket-counter analogy and simple visual representations.

Learn to implement a queue using two stacks to preserve first-in, first-out order, as shown in LeetCode 232, and master the dequeue transfer between stacks.

Implement a first in first out queue using two stacks, providing push, peek, pop, and empty operations. Manage element transfer between stack one and stack two to maintain order.