Data Structures & Algorithms
Arrays, linked lists, stacks, trees, graphs, sorting and searching — the core problem-solving toolkit for developers.
Data Structures & Algorithms (DSA) is the difference between someone who can write code and someone who can solve problems. It's how you store data sensibly and process it efficiently — and it's the skill that interviews, contests, and real engineering all quietly test.
Start with the mental model: what DSA actually is and Big-O / time complexity explained simply. Then work through the core structures — arrays, linked lists, stacks, queues, trees and graphs — and the algorithms that power them: sorting, searching and hashing. New to it? Follow our beginner DSA roadmap.
We teach DSA hands-on in C++ and Java with steady practice, so it stops feeling abstract and starts feeling like a tool you reach for. It's the single highest-leverage thing you can learn to become a genuinely better programmer.
Want guided practice? Join the waitlist for our Data Structures & Algorithms course in Jalgaon.
Arrays as a Data Structure
A clear introduction to arrays: how contiguous storage gives O(1) random access, why inserting in the middle is O(n), and when to reach for an array versus another structure.
4 min readBig-O Notation & Time Complexity, Simply Explained
A plain-language guide to Big-O notation and time complexity, covering the common growth rates with worked examples so you can reason about how fast your code really is.
4 min readBinary Search Trees Explained
Understand the binary search tree: how the left-smaller, right-larger rule enables O(log n) search on a balanced tree, and why balance is the whole game.
4 min readBubble Sort Explained
Understand bubble sort: how repeated adjacent swaps push the largest values to the end, why it is O(n2), and the early-exit optimisation for nearly sorted data.
4 min readCircular Queues Explained
Understand the circular queue: how wrapping the front and rear indices with modulo reuses freed slots, keeping enqueue and dequeue O(1) in fixed memory.
4 min readDoubly Linked Lists Explained
Understand the doubly linked list: how the extra prev pointer enables backward traversal and O(1) deletion of a known node, and the memory cost that comes with it.
4 min readA Beginner's DSA Roadmap
A step-by-step roadmap for learning data structures and algorithms from scratch: what to study first, what builds on what, and how to practise effectively.
4 min readWhat Are Data Structures & Algorithms?
Data structures organise data; algorithms are the step-by-step methods that act on it. Together they let you solve problems in ways that stay fast and predictable as the data grows.
4 min readGraphs Explained
An introduction to graphs: vertices and edges, directed versus undirected, how adjacency lists store them, and how breadth-first search explores them.
4 min readHashing & Hash Tables
Understand hashing and hash tables: how a hash function maps keys to buckets for average O(1) lookup, how collisions are resolved, and the worst-case caveat.
5 min readInsertion & Selection Sort
Compare insertion sort and selection sort: two simple O(n2) algorithms, how each builds the sorted result, and why insertion sort adapts to nearly sorted data.
5 min readLinear vs Binary Search
Compare linear and binary search: linear scans every element in O(n), binary halves a sorted array in O(log n). Learn the trade-offs and when each is the right tool.
5 min readLinked Lists Explained
Learn how a singly linked list chains nodes together with pointers, why head insertion is O(1) while indexing is O(n), and how it compares with arrays.
4 min readMerge Sort Explained
Understand merge sort: how recursively splitting and merging sorted halves yields a guaranteed O(n log n) stable sort, and the O(n) extra space it requires.
4 min readQueues Explained
Understand the queue: a first-in-first-out structure with O(1) enqueue and dequeue, how it differs from a stack, and where it appears in scheduling and buffering.
4 min readQuick Sort Explained
Understand quick sort: how choosing a pivot and partitioning around it sorts in place, why it averages O(n log n), and how a poor pivot triggers the O(n2) worst case.
4 min readRecursion Problems & Patterns
Understand recursion: how a function solves a problem by calling itself on a smaller input, why every recursion needs a base case, and the patterns that recur.
4 min readStacks Explained
Understand the stack: a last-in-first-out structure with O(1) push and pop, the operations it supports, and where it shows up from undo buttons to the call stack.
4 min readTrees Explained
An introduction to trees: the vocabulary of roots, children, leaves and height, what makes a binary tree, and how the three depth-first traversals visit every node.
4 min read
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