Data Structures & Algorithms Roadmap

Pick a Language

Pick a programming language to practice data structures and algorithms with. You should pick a language that you are comfortable with or plan to adopt.

C#

C (pronounced "C sharp") is a general purpose programming language made by Microsoft. It is used to perform different tasks and can be used to create web apps, games, mobile apps, etc.

JavaScript

JavaScript allows you to add interactivity to your pages. Common examples that you may have seen on the websites are sliders, click interactions, popups and so on. Apart from being used on the frontend in browsers, there is Node.js which is an open-source, cross-platform, back-end JavaScript runtime environment…

C++

C++ is a powerful general-purpose programming language. It can be used to develop operating systems, browsers, games, and so on. C++ supports different ways of programming like procedural, object-oriented, functional, and so on. This makes C++ powerful as well as flexible.

Java

Java is general-purpose language, primarily used for Internet-based applications. It was created in 1995 by James Gosling at Sun Microsystems and is one of the most popular options for backend developers.

Python

Python is a well known programming language which is both a strongly typed and a dynamically typed language. Being an interpreted language, code is executed as soon as it is written and the Python syntax allows for writing code in functional, procedural or object-oriented programmatic ways.

Go

Go is an open source programming language supported by Google. Go can be used to write cloud services, CLI tools, used for API development, and much more.

Rust

Rust is a modern systems programming language focusing on safety, speed, and concurrency. It accomplishes these goals by being memory safe without using garbage collection.

Ruby

Ruby is a high-level, interpreted programming language that blends Perl, Smalltalk, Eiffel, Ada, and Lisp. Ruby focuses on simplicity and productivity along with a syntax that reads and writes naturally. Ruby supports procedural, object-oriented and functional programming and is dynamically typed.

Programming Fundamentals

Programming Fundamentals are the basic concepts and principles that form the foundation of any computer programming language. These include understanding variables, which store data for processing, control structures such as loops and conditional statements that direct the flow of a program, data structures which…

Language Syntax

Language syntax refers to the set of rules that dictate how programs written in a particular programming language must be structured. This can include rules for how to declare variables, how to call functions, how to structure control flow statements, and so on. Syntax varies significantly between different…

Control Structures

Control structures are fundamental elements in most programming languages that facilitate the flow of control through a program. There are three main types of control structures: Sequential, Selection and Iteration.

Pseudo Code

Pseudo code is a plain language description of the steps in an algorithm or another system. It is intended for human reading rather than machine reading. Pseudo code often uses control structures and terms common to popular high-level programming languages without strictly adhering to the syntax of any particular…

Functions

Functions in programming are named sections of a program that perform a specific task. They allow us to write a piece of code once and reuse it in different places throughout the program, making our code more modular and easier to maintain. Functions often take in input, do something with it, and return output.…

OOP Basics

Object-oriented programming (OOP) is a programming paradigm that uses "objects" to design applications and software. In OOP, each object is an instance of a class. A class defines the properties (often known as attributes or fields) and methods (actions) that are common to all objects of a certain kind. A key…

What are Data Structures?

Data structures are specialized formats for organizing and storing data in a computer so that it can be used efficiently. They provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services. They are critical to programming and are used in almost all…

Why are Data Structures Important?

Data structures are crucial in the field of computer science and coding because they offer a method of organizing and storing data in an efficient and manageable format. They're critical because they form the foundation for modern algorithm design. Your ability to choose or design the most suited data structure for…

Basic Data Structures

The five main types of basic data structures are: Arrays, Linked Lists, Stacks, Queues, and Hash Tables.

Array

Linked Lists

Linked Lists are a type of data structure used for storing collections of data. The data is stored in nodes, each of which contains a data field and a reference (link) to the next node in the sequence. Structurally, a linked list is organized into a sequence or chain of nodes, hence the name. Two types of linked…

Queues

Queues are a type of data structure in which elements are held in a sequence and access is restricted to one end. Elements are added ("enqueued") at the rear end and removed ("dequeued") from the front. This makes queues a First-In, First-Out (FIFO) data structure. This type of organization is particularly useful…

Stacks

A stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO (Last In First Out) or FILO (First In Last Out). Mainly three basic operations are performed in the stack:

Hash Tables

Hash Tables are specialized data structures that allow fast access to data based on a key. Essentially, a hash table works by taking a key input, and then computes an index into an array in which the desired value can be found. It uses a hash function to calculate this index. Suppose the elements are integers and…

Algorithmic Complexity

"Algorithmic Complexity" refers to the computing resources needed by an algorithm to solve a problem. These computing resources can be the time taken for program execution (time complexity), or the space used in memory during its execution (space complexity). The aim is to minimize these resources, so an algorithm…

Time vs Space Complexity

In the context of algorithmic complexity, "time" refers to the amount of computational time that the algorithm takes to execute, while "space" refers to the amount of memory that the algorithm needs to complete its operation. The time complexity of an algorithm quantifies the amount of time taken by an algorithm to…

How to Calculate Complexity?

The process of calculating algorithmic complexity, often referred to as Big O notation, involves counting the operations or steps an algorithm takes in function of the size of its input. The aim is to identify the worst-case, average-case, and best-case complexity. Generally, the main focus is on the worst-case…

Asymptotic Notation

Asymptotic notation is a way to describe the limiting behavior of a function, typically representing the time or space complexity of an algorithm, as the input size grows. It focuses on the dominant terms and ignores constant factors and lower-order terms, providing a simplified and generalized way to compare the…

Big-O Notation

"Big O" notation, officially known as O-notation, is used in computer science to describe the performance or complexity of an algorithm. Specifically, it provides an upper bound on the time complexity, describing the worst-case scenario. Thus, it gives an upper limit on the time taken for an algorithm to complete…

Big-θ Notation

Big Theta (θ) notation is used in computer science to describe an asymptotic tight bound on a function. This essentially means it provides both an upper and lower bound for a function. When we say a function f(n) is θ(g(n)), we mean that the growth rate of f(n) is both bounded above and below by the function g(n)…

Big-Ω Notation

The Big Omega (Ω) notation is used in computer science to describe an algorithm's lower bound. Essentially, it provides a best-case analysis of an algorithm's efficiency, giving us a lower limit of the performance. If we say a function f(n) is Ω(g(n)), it means that from a certain point onwards (n0 for some…

Common Runtimes

"Algorithmic Complexity" refers to the computing resources needed by an algorithm to solve a problem. These computing resources can be the time taken for program execution (time complexity), or the space used in memory during its execution (space complexity). The aim is to minimize these resources, so an algorithm…

Constant

Constant time complexity is denoted as O(1). This means the running time of the algorithm remains constant, regardless of the size of the input data set. Whether you're working with an array of 10 elements or 1 million, if an operation takes the same amount of time regardless of the size of the array, it is said to…

Logarithmic

Logarithmic time complexity (O(log n)) often indicates that the algorithm halves the size of the input at each step. It's more efficient compared to linear time complexity. Binary search is a classic example of logarithmic time complexity where at every step, the algorithm breaks the list into half until it finds…

Linear

Linear time complexity, denoted as O(n), is one of the best possible algorithmic performance situations. An algorithm is said to have a linear time complexity when the running time increases at most linearly with the size of the input data. This means that if you double the size of the input, the running time will…

Polynomial

Polynomial time complexity, denoted as O(n^k), is a class of time complexity that represents the amount of time an algorithm takes to run as being proportional to the size of the input data raised to a constant power 'k'. Polynomial time complexity includes runtimes like O(n), O(n^2), O(n^3), etc. The value 'n' is…

Exponential

Exponential time complexity is denoted as O(2^n), where a growth in n leads to an exponential growth in the number of steps required to complete a task. It means that the time complexity will double with every additional element in the input set. This is seen in many recursive algorithms, where a problem is divided…

Factorial

Factorial, often denoted as n!, is a mathematical operation. In the context of computer science and algorithm complexity, it represents an extremely high growth rate. This occurs because of the way a factorial is calculated: The product of all positive integers less than or equal to a non-negative integer n. Thus,…

Sorting Algorithms

Sorting algorithms are used to rearrange a given array or list elements according to a comparison operator on the elements. The comparison operator is used to decide the new order of element in the respective data structure. For example, the numerical order is a commonly used sequence but a lexicographical order is…

Search Algorithms

Search algorithms are techniques used for finding a specific item or group of items among a collection of data. The primary types of search algorithms are linear search, binary search, depth-first search, and breadth-first search. Linear and binary search are explained in this section. The other two types in a next…

Binary Search

Binary Search is a type of search algorithm that follows the divide and conquer strategy. It works on a sorted array by repeatedly dividing the search interval in half. Initially, the search space is the entire array and the target is compared with the middle element of the array. If they are not equal, the half in…

Linear Search

Linear search is one of the simplest search algorithms. In this method, every element in an array is checked sequentially starting from the first until a match is found or all elements have been checked. It is also known as sequential search. It works on both sorted and unsorted lists, and does not need any…

Graph Data Structures

A Graph Data Structure consists of a set of vertices (or nodes) and edges where each edge connects a pair of vertices. It can be visualized as networks consisting of elements in interconnected various relationships. There are two major types of graphs: Directed and Undirected. In a directed graph, all the edges are…

Directed Graph

A Directed Graph, also known as a DiGraph, is a set of vertices and a collection of directed edges. Each directed edge has an initial vertex, also called the tail, and a terminal vertex, also known as the head. The directed edge is said to point from the tail to the head. To visualize this, think of a graph where…

Undirected Graph

An Undirected Graph is a type of graph in which the edges are not directed. That is, they do not point in any specific direction and are not ordered pairs. They cannot be referred to as originating or ending node, instead, they are endpoints of the edges. In this type of graph, the edges essentially represent a…

Bubble Sort

Bubble Sort is a simple sorting algorithm that works by repeatedly swapping the adjacent elements if they are in the wrong order. It gets its name because with each iteration the largest element "bubbles" up to its proper location. It continues this process of swapping until the entire list is sorted in ascending…

Merge Sort

Merge sort is a type of sorting algorithm that follows the divide-and-conquer paradigm. It was invented by John von Neumann in 1945. This algorithm works by dividing an unsorted list into n partitions, each containing one element (a list of one element is considered sorted), then repeatedly merging partitions to…

Insertion Sort

Insertion sort is a simple sorting algorithm that works by iteratively inserting each element of an unsorted list into its correct position in a sorted portion of the list. It is like sorting playing cards in your hands. You split the cards into two groups: the sorted cards and the unsorted cards. Then, you pick a…

Quick Sort

Quicksort, also known as partition-exchange sort, is an efficient, in-place sorting algorithm, which uses divide and conquer principles. It was developed by Tony Hoare in 1959. It operates by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays, according to whether…

Selection Sort

Selection Sort is a simple and intuitive sorting algorithm. It works by dividing the array into two parts - sorted and unsorted. Initially, the sorted part is empty and the unsorted part contains all the elements. The algorithm repeatedly selects the smallest (or largest, if sorting in descending order) element…

Heap Sort

Heap Sort is an efficient, comparison-based sorting algorithm. It utilizes a data structure known as a 'binary heap', and works by dividing its input into a sorted and an unsorted region, and iteratively shrinking the unsorted region by extracting the largest element and moving that to the sorted region. It's an…

Tree Data Structures

A Tree data structure is a type of non-linear, hierarchical data structure that consists of nodes connected by edges. It follows the parent-child relationship, with the top node being called the root. Each node in a tree can have child nodes and each of these child nodes has a single parent node. Nodes with same…

Advanced Data Structures

Advanced data structures are an integral part of advanced programming. They include structures such as Binary Search Trees (BST), Balanced trees like AVL Trees, Red-Black Trees, Heap, Disjoint Data Set, Trie, Suffix Array, Suffix tree and others. BST is a node-based data structure where each node contains a key, a…

Trie

A Trie, also called digital tree and sometimes radix tree or prefix tree, is a type of search tree that is used to store a dynamic set or associative array where the keys are usually strings. Unlike binary search trees, no node in the tree stores the key associated with that node; instead, its position in the tree…

Segment Trees

A Segment Tree is a data structure designed for efficient processing of range queries and updates on array elements. In a situation where you have an array and you need to execute several types of queries including updating array elements and computing sum or minimum or maximum of elements in a given range, segment…

Fenwick Trees

Fenwick Trees, also known as Binary Indexed Trees, are data structures that can efficiently support the operation of updating elements and calculating prefix sums in a table of numbers. This makes it particularly useful in situations where the table gets updated often and different kinds of queries (such as sum of…

Disjoint Set (Union-Find)

A disjoint-set data structure, also called a union-find data structure or merge-find set, is a data structure that tracks a partition of a set into numerous non-overlapping subsets. It provides near-constant-time operations to add new sets, to merge existing sets, and to determine whether elements are in the same…

Suffix Trees and Arrays

Suffix Trees and Arrays are advanced data structures used primarily for string manipulation and searches. A Suffix Tree is a compressed trie containing all the suffixes of a given text as their keys and positions as their values. On the other hand, a Suffix Array is a sorted array of all possible suffixes of a…

Binary Trees

A Binary Tree is a type of tree data structure in which each node has at most two children, referred to as the left child and the right child. This distinguishes it from trees in which nodes can have any number of children. A binary tree is further classified as a strictly binary tree if every non-leaf node in the…

Tree Traversal

A Tree data structure is a type of non-linear, hierarchical data structure that consists of nodes connected by edges. It follows the parent-child relationship, with the top node being called the root. Each node in a tree can have child nodes and each of these child nodes has a single parent node. Nodes with same…

Search Algorithms

A Tree data structure is a type of non-linear, hierarchical data structure that consists of nodes connected by edges. It follows the parent-child relationship, with the top node being called the root. Each node in a tree can have child nodes and each of these child nodes has a single parent node. Nodes with same…

Binary Search Trees

A Binary Search Tree (BST) is a type of binary tree data structure where each node carries a unique key (a value), and each key/node has up to two referenced sub-trees, the left and right child. The key feature of a BST is that every node on the right subtree must have a value greater than its parent node, while…

In-Order Traversal

In order traversal is a method for traversing binary trees. This method follows a specific order: Left Node, Root Node, then Right Node (L-N-R). Starting from the leftmost node of the tree, you first visit the left subtree, then the root node, and finally the right subtree. If the tree is a binary search tree, in…

Breadth First Search

Breadth-First Search (BFS) is a searching algorithm that's used in tree or graph data structures. It starts from the root (the topmost node in the tree) and expands all neighboring nodes at the present depth prior to moving on to nodes at the next depth level. This technique uses a queue data structure to remember…

AVL Trees

An AVL tree is a type of binary search tree that is self-balancing, which means the heights of the two child subtrees of any node in the tree differ by at most one. If at any point the difference becomes greater than one, rebalancing is done to restore the property. The tree is named after its inventors, G.M.…

Pre-Order Traversal

Pre Order Traversal is a very specific kind of tree traversal in data structures. In this method of traversal, the process starts from the root node, then proceeds to the left subtree, and finally to the right subtree. To put it concisely, the order of traversal is Root, Left, and Right (often abbreviated as…

Depth First Search

Depth First Search (DFS) is an algorithm for traversing or searching tree or graph data structures. The process starts at the root (in the case of a tree) or an arbitrary node (in case of a graph), and explores as far as possible along each branch before retracing steps. Essentially, DFS is about diving deep into…

B-Trees

B-Tree is a self-balanced search tree data structure that maintains sorted data and allows for efficient insertion, deletion, and search operations. It is most commonly used in systems where read and write operations are performed on disk, such as databases and file systems. The main characteristic of a B-Tree is…

Heap

A heap is a type of data structure in computer science that is like a tree, where each parent node is always bigger (in a max heap) or smaller (in a min heap) than its child nodes.

Post-Order Traversal

Post Order Traversal is a technique used in Binary Tree structure where each node is processed after its child nodes. As the name suggests, it first traverses the left subtree, then the right subtree, and finally the root node. The process is recursively repeated for each subtree until the entire tree has been…

Search Algorithms

A Graph Data Structure consists of a set of vertices (or nodes) and edges where each edge connects a pair of vertices. It can be visualized as networks consisting of elements in interconnected various relationships. There are two major types of graphs: Directed and Undirected. In a directed graph, all the edges are…

Breadth First Search

Breadth-First Search (BFS) is a searching algorithm used to traverse or search in data structures like a tree or a graph. The algorithm starts with a root node and visits the nodes in a level by level manner (i.e., visiting the ones nearest to the root first). It makes use of a queue data structure to store nodes…

Depth First Search

Depth-First Search (DFS) is an algorithm used for traversing or searching tree or graph data structures. The process starts at the root node (selecting some arbitrary node as the root in the case of a graph), and explores as far as possible along each branch before backtracking. It uses a last in, first out (LIFO)…

Shortest Path Algorithms

A Graph Data Structure consists of a set of vertices (or nodes) and edges where each edge connects a pair of vertices. It can be visualized as networks consisting of elements in interconnected various relationships. There are two major types of graphs: Directed and Undirected. In a directed graph, all the edges are…

Dijkstra's Algorithm

Dijkstra's algorithm is a popular method used in computing and graph theory for finding the shortest paths between nodes in a graph. Named after Dutch computer scientist Edsger W. Dijkstra, this algorithm works by visiting vertices in the graph starting from the object's starting point and gradually spreading out…

Bellman-Ford Algoritm

The Bellman Ford algorithm is a method used in graph theory for finding the shortest path between a single source vertex and all other vertices in a weighted graph. This algorithm is significant because it is capable of handling graphs with negative weight edges, unlike Dijkstra's algorithm. It follows a bottom-up…

A* Algorithm

A is a pathfinding and graph traversal algorithm that finds the shortest path between two nodes. It combines the guarantees of Dijkstra's algorithm with a heuristic that estimates the remaining distance, guiding the search toward the goal and exploring far fewer nodes in practice.

Minimum Spanning Tree

A Graph Data Structure consists of a set of vertices (or nodes) and edges where each edge connects a pair of vertices. It can be visualized as networks consisting of elements in interconnected various relationships. There are two major types of graphs: Directed and Undirected. In a directed graph, all the edges are…

Prim's Algorithm

Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. This means it finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized. The algorithm operates by building this tree one vertex…

Kruskal's Algorithm

Kruskal's algorithm is a popular procedure in computer science for finding minimum spanning trees in a graph, developed by Joseph Kruskal in 1956. The algorithm operates by sorting the edges of the graph by their weight in ascending order. Then, it loops through each, adding the edge to the spanning tree if it…

Complex Data Structures

Complex data structures are advanced structures that are used for storing and organizing data in a more specialized way to manage larger amounts of data more effectively. These include Trees, Graphs, Hash Tables, and Heaps. Trees allow for hierarchical data structures and can be utilized in many ways like Binary…

2-3 Trees

Complex data structures are advanced structures that are used for storing and organizing data in a more specialized way to manage larger amounts of data more effectively. These include Trees, Graphs, Hash Tables, and Heaps. Trees allow for hierarchical data structures and can be utilized in many ways like Binary…

B/B+ Trees

B trees and B+ trees are both types of self-balancing, sorted, tree-based data structures that maintain sorted data in a way that allows for efficient insertion, deletion, and search operations. A B tree is a tree data structure in which each node has multiple keys and can be in more than two children nodes. Each…

Skip List

A Skip List is a probabilistic data structure that allows efficient search, insertion, and removal operations. It is a layered list that consists of a base list holding all the elements and several lists layered on top, each layer containing a random subset of the elements from the layer below. The highest level…

ISAM

ISAM, which stands for Indexed Sequential Access Method, is a type of disk storage access method developed by IBM. It combines features of both sequential and direct access methods to store and retrieve data. ISAM primarily organizes data sequentially but creates an index to provide direct access to the data…

Indexing

Indexing is a data structure technique to efficiently retrieve data from a database. It essentially creates a lookup that can be used to quickly find the location of data records on a disk. Indexes are created using a few database columns and are capable of rapidly locating data without scanning every row in a…

Linear

Linear indexing is a type of data structure method where each element of an array is indexed successively in a linear format. This method treats multi-dimensional arrays as a long vector and provides a simple way to traverse through all the elements of the array in a sequence without the need for multiple loop…

Tree-Based

Tree-based indexing involves using data structures that follow a tree-like model, with parent nodes, child nodes, and leaf nodes in a hierarchical order. Two popular tree-based indexing methods are B-tree and B+ tree. In a B-tree index, every data record is associated with a leaf node, while internal nodes can be…

Problem Solving Techniques

"Problem Solving Techniques" is a topic that explore various methods used to solve problems in computing and mathematics. They are approaches and procedures that help in the systematic identification and resolution of complex issues. Some of these techniques include "Divide and Conquer" where a problem is split…

Brute Force

"Brute Force" is a straightforward method to solve problems. It involves trying every possible solution until the right one is found. This technique does not require any specific skills or knowledge and the approach is directly applied to the problem at hand. However, while it can be effective, it is not always…

Backtracking

Backtracking is a powerful algorithmic technique that aims to solve a problem incrementally, by trying out an various sequences of decisions. If at any point it realizes that its current path will not lead to a solution, it reverses or "backtracks" the most recent decision and tries the next available route.…

Greedy Algorithms

Greedy algorithms follow the problem-solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum. They are used for optimization problems. An optimal solution is one where the value of the solution is either maximum or minimum. These algorithms work in a " greedy"…

Island traversal

Island traversal is a grid-based technique used to find connected regions of cells that share a common value, typically 1s in a binary matrix. It combines grid traversal with depth-first or breadth-first search — often with visited tracking — to count islands, measure their size, or analyze their shape.

Randomised Algorithms

Randomised algorithms are a type of algorithm that employs a degree of randomness as part of the logic of the algorithm. These algorithms use random numbers to make decisions, and thus, even for the same input, can produce different outcomes on different executions. The correctness of these algorithms are…

Multi-threaded

Multi-threaded algorithms divide work across multiple threads to run faster on multi-core machines. Common patterns include parallel divide-and-conquer, thread-safe queues for producer-consumer flows, and careful synchronization to avoid race conditions — with quality often measured by speedup and scalability.

Divide and Conquer

Divide and conquer is a powerful algorithm design technique that solves a problem by breaking it down into smaller and easier-to-manage sub-problems, until these become simple enough to be solved directly. This approach is usually carried out recursively for most problems. Once all the sub-problems are solved, the…

Two Heaps

The two heaps method uses a max-heap to store the lower half of the numbers and a min-heap to store the upper half. This setup allows you to quickly access the largest value of the lower half and the smallest value of the upper half in constant time. Insertions and deletions take logarithmic time, and the heaps are…

Kth Element

Kth element problems ask you to find the kth smallest (or largest) element in a collection without fully sorting it. Common solutions include heaps, quickselect, or sorting, depending on the constraints — a pattern that also appears in questions about arrays, streams, and binary search trees.

Recursion

Recursion is a method where the solution to a problem depends on solutions to shorter instances of the same problem. It involves a function calling itself while having a condition for its termination. This technique is mostly used in programming languages like C++, Java, Python, etc. There are two main components…

Merge Intervals

The merge intervals pattern deals with overlapping ranges, typically merging a list of intervals that intersect. The standard approach sorts the intervals by start time and then folds them together in a single pass — a technique used in scheduling, availability, and range problems.

Dynamic Programming

Dynamic Programming is a powerful problem-solving method that solves complex problems by breaking them down into simpler subproblems and solving each subproblem only once, storing their results using a memory-based data structure (like an array or a dictionary). The principle of dynamic programming is based on…

Cyclic Sort

Cyclic sort is a sorting technique for arrays whose values fall in a known, contiguous range, typically 1 to n. Each element is swapped to the position matching its value, placing everything correctly in O(n) time and O(1) space — ideal as a building block for problems such as finding missing or duplicate numbers.

Two Pointer Technique

The two-pointer technique is a strategy that can be used to solve certain types of problems, particularly those that involve arrays or linked lists. This technique primarily involves using two pointers, which navigate through the data structure in various ways, depending on the nature of the problem. The pointers…

Fast and Slow Pointers

The fast and slow pointers pattern uses two pointers that traverse a data structure at different speeds. It is commonly used to detect cycles in linked lists, find the middle of a list, or locate the kth element from the end, running in O(n) time with constant space.

Sliding Window Technique

The Sliding Window Technique is an algorithmic paradigm that manages a subset of items in a collection of objects, like an array or list, by maintaining a range of elements observed, which is referred to as the 'window'. The window 'slides' over the data to examine different subsets of its contents. This technique…

Platforms to Practice

There are numerous platforms for practicing data structures and algorithms. Some of the notable ones include LeetCode, with a vast collection of challenges and a robust community, and HackerRank, which provides a diverse set of problems and hosts competitive coding contests. Other platforms such as CodeChef,…

Leetcode

LeetCode is a widely recognized online platform used for preparing coding interviews and improving problem-solving skills. It offers a vast collection of programming challenges that can be solved in multiple programming languages. The problems are categorized by difficulty level, and each problem has a solution…

Edabit

Edabit is an online platform designed to improve coding skills for various programming languages like JavaScript, Python, C, PHP, Java, Swift, Ruby, and C++. This platform is centered around practice and engagement, through interactive challenges that assist in learning the syntax, structure, and basic concepts of…