Introduction To The Data Structure
The data structure is the collection of data elements that effectively organize and store data in a computer to be used effectively. Data structures include arrays, Linked Lists, Stacks, Queues, etc. Data structures are employed extensively in every area of computer science, including operating systems, compiler design, artificial intelligence, graphics, and many others.
Many algorithms in computer science rely heavily on data structures because they give programmers the tools they need to handle data effectively. As the primary purpose of the software is to store and retrieve user data quickly, it is essential for improving the performance of software or programs.
Basic Terminology:
The fundamental units of any program or piece of software are data structures. The most challenging challenge for a programmer is selecting the right data structure for a program. Regarding data structures, the terms listed below are used.
Data: Data is a basic value or a group of values. For instance, a student's name and student id are examples of data about a student.
Group Items: Group items are data items that have subordinate data items. For instance, a student's name may include their first and last names.
If we talk about the student object, then its name, address, course, and marks can be gathered together to make the record for the student. Record: A record can be defined as the collection of numerous data items.
File: A file is a collection of different records for a single type of entity; for instance, if a class has 60 employees, the relevant file will contain 20 entries, each of which contains information about a different employee.
Entity and Attribute: An entity depicts the category of specific items. It has several characteristics. Every attribute stands for a specific property of that entity. Each of these will be covered in more detail later in a data structure course by Learnbay.
Field: A field is a single elementary data unit representing an entity's attribute.
Data structures are required.
Due to the complexity of applications and the growing volume of data, the following issues could arise:
Processor speed: Fast processing is needed to manage huge amounts of data. However, because the volume of data is increasing daily to billions of files for every entity, the processor may not be able to handle so much data.
Data Search: Assume that there are 106 things in the store's inventory. If our program has to look for a specific item, it must go through all 106 of them, which slows down the search process.
Multiple requests: There is a potential that a very large server could break during that process if hundreds of users are simultaneously searching the data on a web server.
Data structures are employed to address the issues above. Data is arranged to create a data structure so necessary data may be promptly searched without having to search through all things.
Advantages of Data Structures:
Efficiency: The choice of data structures affects how efficient a programme is. For instance, let's say we need to search through certain data to find a specific record. If we arrange our data in an array in that instance, we will need to search element by element sequentially. In this case, employing an array may not be very effective. Better data structures, such as ordered arrays, binary search trees, or hash tables, can improve search efficiency.
Reusability: Data structures are reusable, meaning that once we have implemented a specific data structure, we can apply it everywhere we need to. Data structure implementations can be compiled into libraries that various clients can utilize.
Abstraction: The ADT specifies the data structure and adds an abstraction layer. The client software uses the data structure's interface; it is not concerned with the implementation's specifics.
Operations on data structure:
Traversing: A set of data items are present in every data structure. In order to carry out a certain operation, such as sorting or searching, traversing the data structure entails going over each element individually.
Example: To determine the average of a student's marks across six different subjects, we must traverse the entire array of marks and total them. We then divide that total by the number of subjects, in this case, 6, to determine the average.
Insertion: The process of adding elements to the data structure at any position is referred to as insertion.
Only n-1 data elements can be added to a data structure with n dimensions.
Deletion: Deletion is the action of eliminating a data structure member. Any random position in the data structure allows us to remove an element.
Underflow happens if we attempt to remove an element from a data structure that is empty.
Searching: Searching refers to the process of locating an element within a data structure. Linear Search and Binary Search are the two search algorithms available.
Sorting: Sorting is the action of placing the data structure in a particular order. Sorting can be done using a variety of algorithms, such as insertion sort, selection sort, bubble sort, etc.
Merging: The process of combining or joining two lists of similar-type elements, List A and List B, each of size M and N, to create List C, of size (M+N), is known as merging.
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