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Tue Apr 12 2022 (6 months ago)

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What is Data structure?

In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, i.e., it is an algebraic structure about data.

Table of contents

The data structure name indicates organizing the data in memory. There are many ways of organizing the data in the memory as we have already seen one of the data structures, i.e., array in C language. The array is a collection of memory elements in which data is stored sequentially, i.e., one after another. In other words, we can say that the array stores the elements in a continuous manner. This organization of data is done with the help of an array of data structures. There are also other ways to organize the data in memory. Let's see the different types of data structures.

Why are data structures important?

Typical base data types, such as integers or floating-point values, that are available in most computer programming languages are generally insufficient to capture the logical intent for data processing and use. Yet applications that ingest, manipulate and produce information must understand how data should be organized to simplify processing. Data structures bring together the data elements in a logical way and facilitate the effective use, persistence and sharing of data. They provide a formal model that describes the way the data elements are organized.

Data structures are the building blocks for more sophisticated applications. They are designed by composing data elements into a logical unit representing an abstract data type that has relevance to the algorithm or application. An example of an abstract data type is a "customer name" that is composed of the character strings for "first name," "middle name" and "last name."

A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need inappropriate ways. Most importantly, data structures frame the organization of information so that machines and humans can better understand it.

In computer science and computer programming, a data structure may be selected or designed to store data for the purpose of using it with various algorithms. In some cases, the algorithm's basic operations are tightly coupled to the data structure's design. Each data structure contains information about the data values, relationships between the data and -- in some cases -- functions that can be applied to the data.

For instance, in an object-oriented programming language, the data structure and its associated methods are bound together as part of a class definition. In non-object-oriented languages, there may be functions defined to work with the data structure, but they are not technically part of the data structure.

Why are data structures important?
Typical base data types, such as integers or floating-point values, that are available in most computer programming languages are generally insufficient to capture the logical intent for data processing and use. Yet applications that ingest, manipulate and produce information must understand how data should be organized to simplify processing. Data structures bring together the data elements in a logical way and facilitate the effective use, persistence and sharing of data. They provide a formal model that describes the way the data elements are organized.

Data structures are the building blocks for more sophisticated applications. They are designed by composing data elements into a logical unit representing an abstract data type that has relevance to the algorithm or application. An example of an abstract data type is a "customer name" that is composed of the character strings for "first name," "middle name" and "last name."

It is not only important to use data structures, but it is also important to choose the proper data structure for each task. Choosing an ill-suited data structure could result in slow runtimes or unresponsive code. Five factors to consider when picking a data structure include the following:

  1. What kind of information will be stored?

  2. How will that information be used?

  3. Where should data persist, or be kept, after it is created?

  4. What is the best way to organize the data?

  5. What aspects of memory and storage reservation management should be considered?

Types of Data Structure

There are mainly two types of data structure:

Types of Data Structure

  1. Primitive Data Structure - Primitive Data Structures directly operate according to the machine instructions. These are the primitive data types. Data types like int, char, float, double, and pointer are primitive data structures that can hold a single value.

  2. Non–Primitive Data Structure - Non-primitive data structures are complex data structures that are derived from primitive data structures. Non – Primitive data types are further divided into two categories.

Types of Non-Primitive Data Structure

There are two types of Non-Primitive data structure:

Types of Non-Primitive Data Structure

  1. Linear Data Structure - Linear Data Structure consists of data elements arranged in a sequential manner where every element is connected to its previous and next elements. This connection helps to traverse a linear arrangement in a single level and in a single run. Such data structures are easy to implement as memory is additionally sequential. Some examples of Linear Data Structure are List, Queue, Stack, Array etc.

  2. Non–Linear Data Structure - Non-linear Data Structures do not have any set sequence of connecting all its elements and every element can have multiple paths to attach to other elements. Such data structures support multi-level storage and sometimes can’t be traversed in a single run. Such data structures aren’t easy to implement but are more efficient in utilizing memory. Some examples of non-linear data structures are Tree, BST, Graphs etc.

Types of Linear Data Structure

There are mainly two types of Linear Data Structure:

Types of Linear Data Structure

  1. Static - A static data structure is an organization or collection of data in memory that is fixed in size. This results in the maximum size needing to be known in advance, as memory cannot be reallocated at a later point. Arrays are a prominent example of a static data structure.

  2. Dynamic - A dynamic data structure (DDS) refers to an organization or collection of data in memory that has the flexibility to grow or shrink in size, enabling a programmer to control exactly how much memory is utilized, eg linked lists, tree, etc...

Types of Non-Linear Data Structure

There are mainly two types of Non-Linear Data Structure:

Types of Non-Linear Data Structure

  1. Tree - A tree is a multilevel data structure defined as a set of nodes. The topmost node is named the root node while the bottom-most nodes are called leaf nodes. Each node has only one parent but can have multiple children.

  2. Graph - A graph is a pictorial representation of a set of objects connected by links known as edges. The interconnected nodes are represented by points named vertices, and the links that connect the vertices are called edges.

Types of static data structure

There is only one type of static data structure, ie array.

Types of static data structure

Array - n computer science, an array data structure, or simply an array, is a data structure consisting of a collection of elements, each identified by at least one array index or key. An array is stored such that the position of each element can be computed from its index tuple by a mathematical formula.

Types of dynamic data structure

There are mainly three types of dynamic data structure:

Types of dynamic data structure

  1. Linked List - a linked list is a linear collection of data elements whose order is not given by their physical placement in memory. Instead, each element points to the next. It is a data structure consisting of a collection of nodes that together represent a sequence.

  2. Stack - a stack is an abstract data type that serves as a collection of elements, with two main principal operations: Push, which adds an element to the collection, and Pop, which removes the most recently added element that was not yet removed.

  3. Queue - In computer science, a queue is a collection of entities that are maintained in a sequence and can be modified by the addition of entities at one end of the sequence and the removal of entities from the other end of the sequence.

Data Structure Operations

As we have data structures, so need to operate on them in order to make it useful.

Data Structure Operations

The common operations that can be performed on the data structures are as follows :

  • Searching – We can easily search for any data element in a data structure.

  • Sorting – We can sort the elements either in ascending or descending order.

  • Insertion – We can insert new data elements into the data structure.

  • Deletion – We can delete the data elements from the data structure.

  • Updation – We can update or replace the existing elements from the data structure.

Advantages of Data Structure

  • Data structures allow storing the information on hard disks.

  • Appropriate choice of ADT (Abstract Data Type) makes the program more efficient.

  • Data Structures are necessary for designing efficient algorithms.

  • It provides reusability and abstraction .

  • Using appropriate data structure, can help programmers save a good amount of time while performing operations such as storage, retrieval or processing of data.

  • Manipulation of large amounts of data is easier.

Data Structure Applications

  1. Organization of data in a computer’s memory

  2. Representation of information in databases

  3. Algorithms that search through data (such as a search engine)

  4. algorithms that manipulate data (such as a word processor)

  5. algorithms that analyze data (such as a data miner)

  6. algorithms that generate data (such as a random number generator)

  7. algorithms that compress and decompress data (such as a zip utility)

  8. algorithms that encrypt and decrypt data (such as a security system)

  9. software that manages files and directories (such as a file manager)

  10. software that renders graphics (such as a web browser or 3D rendering software)

Conclusion

We have understood what exactly data structure is. We have seen various kinds of data structures and their types, advantages, disadvantages, applications, etc...

AlgorithmsDesign And Analysis of AlgorithmsData Structure

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