What Is Data Structure? Types, Classification, and Applications

Introduction 

In today’s competitive and challenging world, data is one of the most powerful tools available to businesses and organizations. It helps overcome problems and obstacles, leading to more options and better solutions. 

Keeping this data organized and easily accessible is important, but it also brings some hefty demands. If you can’t turn your data into actionable assets, all the data in the world won’t help you make the right business decision. 

There is a huge demand for Data Scientists in today’s economy since data fuels today’s engines of commerce. Data Scientists are in high demand, with a 650% increase in their demand since 2012. In today’s market, Data Scientists are among the hottest career options one can indulge in. 

One of the most important aspects of Data Science is Data Structure. This article will define, explore, classify, and explain data structures. There will even be a discussion of what Is data structure types and linear and non-linear data structures. 

What Is Data Structure? 

Let’s define data structure. A data structure is a way of organizing various types of data in memory. A data structure is a way to organize data in memory so that it can be used to perform several operations efficiently. Information is managed, processed, and efficiently obtained using it. 

Almost every data structure will consist of two basic components: data structures types and the various operations performed on that data. 

Importance of Primitive Data Structure 

• Storage devices are more effective when data is structured properly 
• Using the right data structure technique, you can manipulate huge amounts of data easily 
• Storage devices can be retrieved more easily by using data structures 
• Data structures can reduce the amount of time a programmer spends storing, retrieving, and processing data 
• In the long run, utilizing data structures might simply result in reusability  

How Does Data Structure Vary From Data Type? 

Our knowledge of abstract data types in data structure has already been acquired. There are many instances wherein people are confused between the type of data and the structure of their data because of this. So, let’s take a closer look at some differences between data structures and data types to understand them better. 

Classification of Data Structures 

Every day, we use data structures in a variety of ways. Mathematical and logical problems are solved using different data structures. Using data structures, one can organize, process, and analyze a lot of data quickly. Here are some types of data structures you might use in different circumstances. 

Linear Data Structure 

Let’s discuss “what is linear data structure?” In a linear data structure, every element is connected to its preceding and next adjacent elements sequentially or linearly. Many linear data structures exist, including arrays, stacks, queues, and linked lists. 

Static Data Structure 

There is a fixed size for static data structures. An element of a static data structure can be easily accessed. Data structures such as arrays are examples of this type. 

Arrays: Arrays allows to the storage of items at adjacent locations in memory. Several items of the same type can be stored in a single location. This technology allows large amounts of data to be processed in a short period of time. A subscript of 0 indicates where the array’s first element is located. You can search, sort, insert, traverse, reverse, and delete data in an array. 

Characteristics of an Array 

• An array is a data structure based on an index, which makes it easy to identify each element in the array. 
• A user can efficiently utilize the array if they wish to store multiple values of the same data type. 
• Arrays make searching very easy. 

Applications of Array 

• Matrix problems can be solved using arrays. 
• Scheduling CPUs can be done using an array. 
• An array is also used to implement database records. 
• Online ticket booking uses the array. This array displays cell phone contacts. 
• An array is also used to display the computer’s screen. Multidimensional arrays are used here. 

Real-Life Applications of Array 

• Data for mathematical computations are often stored in an array. 
• Records are also managed using it. 
• Image processing involves the use of this array algorithm. 
• Boxes can also be ordered using it. 

Dynamic data structure 

Data structures with dynamic sizes do not have fixed sizes. A random update can be performed during the runtime, which can be considered efficient considering the code’s memory complexity. Several examples of this type of data structure include queues and stacks. 

Linked List: Data structures such as linked lists do not store elements in contiguous locations in memory. A linked list uses pointers to link the elements, as shown in the following image: 

Characteristics of a Linked List 

• In a linked list, links are stored in extra memory. 
• When an element is initialized in a linked list, it is not necessary to know its size. 
• Linked lists have a Head node at the beginning. 
• There is always a NULL pointer as the next value of the last node. 
• Adding and deleting items is easy in a linked list. 
• Linked lists can be easily expanded or contracted at any time. 
• A linked list consists of nodes, each of which has an address that corresponds to another node. 

Applications of Linked List 

• There are a number of uses for linked lists, including stacks, graphs, queues, etc. 
• Memory management is made easier with it. 
• When long integers are arithmetically manipulated, linked lists are used. 
• The file allocation is linked using this linked list method. 
• Using them, you can store the history of pages you’ve visited. 
• Links in software development indicate the syntax of a tag. 

Real-Life Applications of Linked List 

• The image viewer uses it. The previous and next images are linked, so the previous and next buttons can be used to access them. 
• Linked lists are used in multiplayer games to track turns in Round-Robin scheduling. 
• Music playlists link songs to each other. 

Stacks: These are linear data structures where operations are performed sequentially. It follows the LIFO principle (Last in, first out). There is only one end from which data can be entered and retrieved. In a stack, data is entered and retrieved by pushing and popping. Reversing a stack, sorting a stack, deleting the middle element of a stack, etc., are all possible operations on a stack. 

Characteristics of a Stack 

• In many algorithms, stacks are used, such as in Tree traversal, Tower of Hanoi, and recursion. 
• This operation uses the Last In, First Out principle, i.e., the element that is inserted first will be the last to pop out and vice versa. 
• Stacks are inserted and deleted from one end, which is the top. 
• Stacks are implemented using arrays or linked lists. 

Applications of Stack 

• For evaluating and converting arithmetic expressions, stack data structures are used. 
• Memory management uses stacks. 
• Recursion uses stacks. 
• The stack is used to convert infix expressions to postfix expressions. 

Real-Life Applications of Stack 

• Sites previously visited are kept in a stack data structure by browsers. 
• Stack data structures are also used in the mobile call log. 

Non-linear Data Structure 

Non-linear data structures do not have data elements arranged sequentially or linearly. It is impossible to traverse all the elements of a non-linear data structure in one run. Non-linear data structure example includes graphs and trees. 

Tree: A tree is a type of data structure that is non-linear and hierarchical. The root node of a tree is the topmost node. Nodes contain data, and the types of data can vary. Nodes are connected via edges from a central node to structural nodes. Nodes are arranged in a tree-like fashion. The tree data structure is a non-linear data structure that provides quicker and easier access to data. 

Characteristics of a Tree 

• There is also a term called recursive data structure used to refer to trees. 
• The height of the root is measured as the longest path between the root node and the leaf node in a tree. 

Applications of Tree 

• Indexing in databases is implemented using B-Trees and B Trees. 
• In K-dimensional space, a K-D Tree organizes points using space partitioning. 
• Compiler designers use syntax trees to scan, parse, generate code, and evaluate arithmetic expressions. 

Real-Life Applications of Tree 

• Databases can also be indexed using it. 
• Domain Name Servers also use a tree data structure. 
• Game development uses tree data structures in real life. 

Conclusion 

Despite these being the most widely known and used data structures, many other types of data structures are also used in Computer Science, such as policy-based data structures. 

The truth is every data structure has its advantages and disadvantages, and if you do not know what they are, choosing the wrong one can be a very costly mistake. Therefore, it is crucial to determine which data structure is best for the situation by understanding the requirements. For an in-depth understanding of these requirements, it is recommended that you pursue UNext Jigsaw’s PG Certificate Program in Data Science and Machine Learning.