Data structures are the fundamental building blocks of computer programming. They define how a program organizes, stores, and manipulates data. Understanding data structures is very important for developing efficient and effective algorithms.
This tutorial will explore the most commonly used data structures, including arrays, linked lists, stacks, queues, trees, and graphs.
What is Data Structure?
A data structure is a storage that is used to store and organize data. It is a way of arranging data on a computer so that it can be accessed and updated efficiently.
A data structure is not only used for organizing the data. It is also used for processing, retrieving, and storing data. Different basic and advanced types of data structures are used in almost every program or software system that has been developed. So we must have good knowledge of data structures.
Classification of Data Structure:
- Linear Data Structure: A data structure in which data elements are arranged sequentially or linearly, where each element is attached to its previous and next adjacent elements, is called a linear data structure.
Example: Array, Stack, Queue, Linked List, etc. - Static Data Structure: Static data structure has a fixed memory size. It is easier to access the elements in a static data structure.
Example: array. - Dynamic Data Structure: In dynamic data structure, the size is not fixed. It can be randomly updated during the runtime which may be considered efficient concerning the memory (space) complexity of the code.
Example: Queue, Stack, etc. - Non-Linear Data Structure: Data structures where data elements are not placed sequentially or linearly are called non-linear data structures. In a non-linear data structure, we can’t traverse all the elements in a single run only.
Examples: Trees and Graphs.
1. Array:
The array is a linear data structure that stores a collection of elements of the same data type. Elements are allocated contiguous memory, allowing for constant-time access. Each element has a unique index number.
- Array Data Structure Guide
- Practice Problems on Arrays
- Top 50 Array Coding Problems for Interviews
2. Matrix/Grid
Matrix is a two-dimensional array of elements, arranged in rows and columns. It is represented as a rectangular grid, with each element at the intersection of a row and column.
- Matrix Data Structure Guide
- Practice Problems on Matrix/Grid
- Top 50 Problems on Matrix/Grid for Interviews
3. String
The string is a sequence of characters, typically used to represent text. It is considered a data type that allows for the manipulation and processing of textual data in computer programs.
- Guide on Strings
- Practice Problems on String
- Top 50 String Coding Problems for Interviews
4. Stack
Stack is a linear data structure that follows the Last In, First Out (LIFO) principle. Stacks play an important role in managing function calls, and memory, and are widely used in algorithms, web development, and systems like compilers and browsers.
- Stack Data Structure Guide
- Practice Problems on Stack
- Top 50 Problems on Stack for Interviews
5. Queue
Queue is a linear data structure that follows the First In, First Out (FIFO) principle. Queues play an important role in managing tasks or data in order, scheduling, and message-handling systems.
- Queue Data Structure Guide
- Practice Problems on Queue
- Top 50 Problems on Queue for Interviews
6. Linked List
The linked list is a linear data structure that stores data in nodes, which are connected by pointers. Unlike arrays, nodes of linked lists are not stored in contiguous memory locations and can only be accessed sequentially, starting from the head of the list.
- Linked List Data Structure Guide
- Practice problems on Linked Lists
- Top 50 Problems on Linked List for Interviews
7. Hash
Hashing is a technique that generates a fixed-size output (hash value) from an input of variable size using mathematical formulas called hash functions. Hashing is commonly used in data structures for efficient searching, insertion, and deletion and plays a key role in software applications like secure data retrieval, password storage, cryptography, digital signatures, etc.
- Guide on Hashing
- Practice Problems on Hashing
- Top 20 Hashing Technique-based Interview Questions
8. Tree
The tree is a non-linear, hierarchical data structure consisting of nodes connected by edges, with a top node, called the root, and nodes having child nodes. It is widely used in file systems, databases, decision-making algorithms, etc.
- Tree Data Structure Guide
- Top 50 Tree Coding Problems for Interviews
9. Binary Tree
A binary Tree is a non-linear and hierarchical data structure where each node has at most two children referred to as the left child and the right child.
- Binary Tree Guide
- Practice Problems on Binary Tree
- MCQs on Binary Trees
10. Binary Search Tree
Binary Search Tree is a type of binary tree in which each node's left subtree contains only values smaller than the node, and each node's right subtree contains only values greater than the node. This property applies recursively, meaning that for every node, its left and right subtrees must also satisfy the conditions of a valid Binary Search Tree.
- Binary Search Tree Guide
- Practice Problems on Binary Search Tree
- Top 50 Binary Search Tree Coding Problems for Interviews
11. Heap
A heap is a complete binary tree data structure that satisfies the heap property. Heaps are usually used to implement priority queues, where the smallest or largest element is always at the root of the tree.
- Heap Data Structure Guide
- Practice Problems on Heap
- Top 50 Problems on Heap for Interviews
12. Graph
The graph is a non-linear data structure consisting of a finite set of vertices(or nodes) and a set of edges(or links)that connect a pair of nodes. Graphs are widely used to represent relationships between entities.
- Graph and its representations
- Practice Problems on Graph
- Top 50 Problems on Graph for Interviews
13. Advanced Data Structures
Advanced Data Structures are complex arrangements of data that are used to organize, store, and manipulate data efficiently, enabling faster and more effective processing in complex algorithms. Unlike basic data types such as arrays and linked lists, these structures include more sophisticated options like Segment Trees, Trie, Binary Indexed Tree, Suffix Arrays, etc.
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