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Core subject guides & interview cheat sheets
Core Subject Modules
The backbone of every technical placement round.
Data Structures & Algorithms (DSA) is about organizing data efficiently and writing procedures that operate on it within acceptable time and space limits. Interviewers use DSA rounds to judge how you break down a problem, choose the right structure, and reason about trade-offs — not just whether your code compiles.
Essential Definitions & Concepts
Array
A fixed-size, contiguous block of memory storing elements of the same type, accessed via index in O(1).
Linked List
A linear structure of nodes where each node points to the next; insertion/deletion is O(1) at a known position but search is O(n).
Tree & BST
A hierarchical structure of nodes with a single root and no cycles; binary search trees keep left < root < right for O(log n) average search.
Graph
A set of vertices connected by edges (directed/undirected, weighted/unweighted), used to model networks, dependencies, and routes.
Time & Space Complexity
A Big-O estimate of how an algorithm's running time or memory usage grows with input size n, independent of hardware.
Dynamic Programming
An optimization technique that solves complex problems by breaking them into overlapping subproblems and storing sub-results (memoization/tabulation).
Core Syllabus Topics & Code Examples
- •Two-pointer and sliding-window techniques for subarray problems
- •In-place array manipulation, matrix rotations, and prefix sums
- •Kadane's algorithm for maximum subarray sum in O(n)
// Kadane's Algorithm for Maximum Subarray Sum
function maxSubArray(nums: number[]): number {
let maxSoFar = nums[0];
let currentMax = nums[0];
for (let i = 1; i < nums.length; i++) {
currentMax = Math.max(nums[i], currentMax + nums[i]);
maxSoFar = Math.max(maxSoFar, currentMax);
}
return maxSoFar;
}