Data Type in Data Structure

 

Data Type

We already have learned about data structure. Many times, what happens is that people get confused between data type and data structure. So let’s see a few differences between data type and data structure to make it clear.

Data Type  Data Structure The data type is the form of a variable to which a value can be assigned. It defines that the particular variable will assign the values of the given data type only. Data structure is a collection of different kinds of data. That entire data can be represented using an object and can be used throughout the program. It can hold value but not data. Therefore, it is dataless. It can hold multiple types of data within a single object. The implementation of a data type is known as abstract implementation. Data structure implementation is known as concrete implementation. There is no time complexity in the case of data types. In data structure objects, time complexity plays an important role. In the case of data types, the value of data is not stored because it only represents the type of data that can be stored. While in the case of data structures, the data and its value acquire the space in the computer’s main memory. Also, a data structure can hold different kinds and types of data within one single object. Data type examples are int, float, double, etc. Data structure examples are stack, queue, tree, etc. Classification of Data Structure:  Data structure has many different uses in our daily life. There are many different data structures that are used to solve different mathematical and logical problems. By using data structure, one can organize and process a very large amount of data in a relatively short period. Let’s look at different data structures that are used in different situations.    Classification of Data Structure Linear data structure: 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.  Examples of linear data structures are 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.  An example of this data structure is an 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.  Examples of this data structure are 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 of non-linear data structures are trees and graphs. Need Of Data structure : The structure of the data and the synthesis of the algorithm are relative to each other. Data presentation must be easy to understand so the developer, as well as the user, can make an efficient implementation of the operation. Data structures provide an easy way of organizing, retrieving, managing, and storing data. Here is a list of the needs for data. Data structure modification is easy.  It requires less time.  Save storage memory space.  Data representation is easy.  Easy access to the large database.