What is Data Structure?
Being a newbie or an expert, the term data structure will be something that will be constantly heard by anyone who’s in computer programming. Understanding the data structures is always critical for becoming a good programmer. A lot of topics are associated with the data structures with a focus on which structures are actually the important ones. Therefore, for being a successful programmer, data structure knowledge is highly recommendable.
Data structure refers to the process whereby the data can be stored and organized in a way that the user can access and utilize the data efficiently. Various algorithms are present to work with the data structures. Therefore, the data structure includes a group of data values, their relation to other elements, and also the operations that can be carried over the data values.
It may be simplified as:
Programs= algorithms + data structures
Data structures=related data + allowed operations on that data
Storage of data can be carried out in two ways. The data structures can be divided into:
- Linear data structure
- Non-linear data structure
Linear Data structure
These are the types of structures where the storage of data takes place sequentially or in a linear fashion. Here, every element stored in the structure is linked to its neighboring elements. The elements can be accessed in a single run as they are arranged linearly. Also, being linearly stored in the memory, implementation is an easy process. The various types are:
The array is a type of data structure that stores elements of the same type. These are the most basic and fundamental data structures. Data stored in each position of an array is given a positive value called the index of the element. The index helps in identifying the location of the elements in an array.
If supposedly we have to store some data i.e. the price of ten cars, then we can create a structure of an array and store all the integers together. This doesn’t need creating ten separate integer variables. Therefore, the lines in a code are reduced and memory is saved. The index value starts with 0 for the first element in the case of an array.
The data structure follows the rule of LIFO (Last In-First Out) where the data last added element is removed first. Push operation is used for adding an element of data on a stack and the pop operation is used for deleting the data from the stack. This can be explained by the example of books stacked together. In order to access the last book, all the books placed on top of the last book have to be safely removed.
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This structure is almost similar to the stack as the data is stored sequentially. The difference is that the queue data structure follows FIFO which is the rule of First In-First Out where the first added element is to exit the queue first. Front and rear are the two terms to be used in a queue.
Enqueue is the insertion operation and dequeue is the deletion operation. The former is performed at the end of the queue and the latter is performed at the start end. The data structure might be explained with the example of people queuing up to ride a bus. The first person in the line will get the chance to exit the queue while the last person will be the last to exit.
4. Linked List
Linked lists are the types where the data is stored in the form of nodes which consist of an element of data and a pointer. The use of the pointer is that it points or directs to the node which is next to the element in the sequence. The data stored in a linked list might be of any form, strings, numbers, or characters. Both sorted and unsorted data can be stored in a linked list along with unique or duplicate elements.
5. Hash Tables
These types can be implemented as linear or non-linear data structures. The data structures consist of key-value pairs.
Non-linear Data Structure
These data structures don’t follow linearity. As the name suggests the data are arranged in a manner that doesn’t follow the contiguous manner. The elements don’t have a set path to connect to the other elements but have multiple paths. Traversing through the elements is not possible in one run as the data is non-linearly arranged.
As compared to the linear structure where an element is connected to both the neighboring elements, in this case, an element can be connected to other elements which don’t need to be only two. Implementation of non-linear data is not easy but computer memory is used efficiently using this type of structure.
The types of structures following non-linearity are Trees and Graphs.
A tree data structure consists of various nodes linked together. The structure of a tree is hierarchical that forms a relationship like that of the parent and a child. The structure of the tree is formed in a way that there is one connection for every parent-child node relationship. Only one path should exist between the root to a node in the tree. Various types of trees are present based on their structures like AVL tree, binary tree, binary search tree, etc.
Graphs are those types of non-linear data structures which consist of a definite quantity of vertices and edges. The vertices or the nodes are involved in storing data and the edges show the vertices relationship. The difference between a graph to a tree is that in a graph there are no specific rules for the connection of nodes. Real-life problems like social networks, telephone networks, etc. can be represented through the graphs.
An adjacency matrix is used for the representation of the Graphs.
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Difference between Linear and Non-linear data structures
We have discussed the linear and non-linear types of data structures. But what are the key points that define linear vs non-linear data structure?
The difference between linear and non-linear data structure is tabulated below:
|Linear Data structure||Non-linear data structure|
|1||The data elements are stored in a linear order in the case of linear data structure. Each and every element is connected to the first and the next element in the sequence.||The data elements in the case of a non-linear data structure are arranged in a non-linear way and attached hierarchically. The data elements are attached to multiple elements.|
|2||The structure of the data consists of a single level. There is no hierarchy in the linear data structure.||In this structure, there are multiple levels involved in the structure. Therefore the elements are arranged hierarchically.|
|3||The implementation of the linear structure of data is easy as the elements are stored in a linear way.||The implementation of the structure is a complex process compared to the linear structure.|
|4||Traversal of the elements in a linear data structure can be carried out in a single execution because the data is present in a single level||Traversal of the elements cannot be carried out in a single execution only. Multiple runs are required for traversing the data in a non-linear data structure.|
|5||There is no efficient utilization of memory in a linear data structure.||There is efficient utilization of memory in a non-linear data structure.|
|6||Examples of linear data structures include array, stack, queues, and linked list.||Examples of non-linear data include trees and graphs|
|7||The linear structure of data is applied mainly in software development.||The non-linear structure of data is mostly applied in Artificial intelligence and image processing.|
|8||With the increase in the size of the input, the time complexity increases.||Even if there is an increase in the size of the input, the time complexity remains the same.|
|9||Only one type of relationship might be present between the data elements||A one-to-one or one-to-many type of relationship can exist between the elements in a non-linear type of data structure.|
Importance of Data structure
Any solid computer programs are built over the concept of structures of data. No program can be efficiently built up without the use of the right data structure. Since there is huge reliability of the computer programs over large volumes of data, efficient storage of the information is required for easy access of data. Application of a data structure allows storing data logically for easy modification and access.
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Data structures have become complex with the increase in the size of the data. The article gave a brief understanding of the types of data structure highlighting the key differences between a linear and a non-linear data structure. However, different data structures have different applications.
The use of the data structure like adding, deleting, accessing elements, modifying elements each have to be studied in-depth to gain an expert understanding of the data structures. However, the first important step towards a good programmer is having a basic understanding of the concept. Learning data structures allows the easy understanding of different programming languages. Be it python, C++, or Java, the concept remains the same.
As it is the era of artificial intelligence, knowledge of machine learning languages is quite important for those who are aiming to work in AI. Storage of data in an efficient form has found applications in the machine learning models. Since, data structures forms the foundation of machine learning programs, understanding it should be the main focus.
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