In today’s era of information technology, data is the most popular term in the industry. 70% of youngsters dream of their careers in data, viz data scientist, data mining, data analyst, big data, etc. The journey to these dreams begins with a comprehensive apprehension of the data. Data is any meaningful information converted into a competent form for processing. Fine-tuned meaningful information, termed data, is used for processing and retaining facts and figures and stored on computers. We store data in a hierarchy of entities, fields, records, and files. An orderly or analytical model of a particular data is called a Data Structure. It is essential to comprehend what is a data structure and its types before diving oneself into algorithms. One should be able to explain data structure and its applications by the end of this session.
Interesting facts about Data Structures
Donald Knuth, an American scientist and winner of the Nobel Prize for Computer Science, is honored as the Father of Data Structures. As a child, he had innovative ways to solve problems in Mathematics. In 1946, in one of the competitions he participated in at his school, he had to create the highest number of meaningful words from the given set of English alphabets. The concept of arranging the letters and creating meaningful words kindled the idea of Data Structures.
In 1957, Klaus Samelson, a German Mathematician, patented the first data structure Stack. Klaus and Friedrich L. Bauer, who later introduced software engineering, were designing translators for the ALGOL programming language.
What are Data Structures
A Data Structure is an organized way of arranging data so that it can be easily operated on, saved, and abstracted as and when required. Structured data facilitates complete visibility, secured access, timely updating, and efficient management.
In other words, a data structure is a fundamental building block for all critical data operations performed on the data.
These illustrations of data structures will help in understanding the concept of a data structure.
Our name is a data structure. It comprises of First name, Middle Name, and Last
The date is a data structure. It covers three data of three types viz date (numeric value), month (character or numeric value), and year (numeric value).
The 11-character code representing the IFSC in the finance industry is a data structure. The first four alphabetic characters in the code indicate the bank name, the fifth character is zero by default, and the last six characters (numeric or alphabetic) are used to represent the
A data structure helps to verify a legitimate passport number. The size of the passport number issued in India is eight characters. The first character cannot be a special character or numeric or a lowercase character, and the second character cannot be zero. The third character must be numeric and can hold a value between zero to
A team of cricket players presents a data structure. The data elements indicate the role of the players viz Captain, Vice-Captain, Bowler, spinner, Wicket Keeper,
Types of data structures
Various data structures are appropriate to certain programs, and some are designed to serve the customers’ specific tasks. Hence choosing the most appropriate data structure results in faster execution. The type of data structure used in a particular situation determines the kind of operations performed or how the algorithms are applied.
Generally, the programmers consider the following factors in the selection of a data structure:
What kind of data will it store?
How will that data be used?
Where should the data be kept?
How can the data be organized in a cost-effective manner?
What aspects of memory and storage management should be considered?
Data structures are classified based on
The processing capacity of the computer - Primitive and Non-primitive Data
The organizing method – Linear (Static and Dynamic) Data Structure and Non-linear Data
|Characteristics||Linear Data Structure||Non-linear Data structure|
|Arrangement of data||The data is stored one after the other in a consecutive manner.||The data is stored in a non-sequential (hierarchical) manner.|
|Storage of data||Each data stored is directly linked to its previous and next elements.||The data is stored in a random manner or in contiguous memory locations.|
|Layers of data||The data is arranged in a single layer.||The data is arranged in multiple layers.|
|Time complexity||The time required to access all the data in the data structure increases as the volume of the data increases.||The time required to access all the data in the data structure remains the same even if the volume of the data increases.|
|Number of traverses||The user can traverse the data structure in a single pass.||The user cannot traverse the data structure in a single pass.|
|Advantages||Irrespective of the volume of the data, these data structures are easy to use where the operations are simple.||Irrespective of the volume of the data, these data structures are easy to use where the operations are complex.|
|Memory usage||Inefficient memory usage||Efficient memory usage|
|Uses||Linear data structures are widely used in software development.||Non-linear data structures are widely used in Artificial intelligence, image processing, etc.|
|Examples||Array, Stack, Queue, LinkedList, etc. Each of these data structures can be further subdivided into its types.||Trees, Graphs, Tables, Sets, etc. Each of these data structures can be further subdivided into its types.|
Why do we need data structures?
The data types in the programming language are inadequate to apprehend the logical purpose for data processing and use. A data structure provides meaning to the data and its
A data structure represents an Abstract Data Type (ADT) that confirms its relevance to the algorithm or application.
A data structure can include all types of data which are significant from the management, organization, and storage perspective. Thus, creating structured data ensures proper access to the respective
A data structure helps in efficient data search and retrieval.
Data structures are the most necessary elements of many robust algorithms.
Benefits of Data Structures
A data structure demonstrates the efficiency of the application or a program.
A data structure exhibits time complexity. Time complexity is the time required to process or pass the data in the data structure. Selection of the correct data structure ensures the lowest time complexity. In other words, the higher the time complexity, the more time is required to execute the program.
A proficient data structure is reusable. The precision in arranging the data in a data structure is the key to designing highly effective algorithms. The efficiency of a data structure directly influences the software design. The reusability of the data structure, in the form of a compiled library, facilitates its users.
Data structures encourage data storage on the disks.
With a proper arrangement of data and the internet, users can access the data anytime from any connected machine (computer, laptop, tablet, phone,etc.)
Disadvantages of Data Structures
Users with proficiency in programming choose to work with data structures. A lack of understanding of the concept of programming makes learning and implementing data structures complex.
The time required to access the data in the data structure depends on the data types. Hence, fundamental knowledge of the data types and their memory consumption is necessary.
Detecting errors or debugging a program with data structures is cumbersome.
Data Structure operations
A data structure obeys a set of rules while organizing data in a computer for its streamlined functioning. Programmers develop algorithms to solve complex problems based on the arrangement of the data in the data structure. The performance of a data structure is independent of the operations performed on it. The critical operations performed on the data structure preserve the data in the data structure and avail it to the users. Some of the operations performed on a data structure are as under-
Traversing- Accessing or visiting each storage location where the data is stored is called traversing. The sequential traversing of a data structure is called a Pass. A traversing displays data or performs operations on the data in the data structure.
Searching- Finding the location(s) of data that satisfies one or more conditions is searching. A user uses various searching methods like a linear search and binary search to detect the position of data in the data structure.
Inserting- Adding new data into the data structure is called inserting. Data is inserted at the initial (first), final (last), or anywhere between the first and the final locations. The size of the data structure increases on account of this operation.
Deleting- Removing existing data from the data structure is called deleting. Data is deleted from the initial (first), final (last), or anywhere between the first and the final locations. The size of the data structure decreases on account of this operation.
Sorting- Arranging the data in a specified order (ascending or descending) is sorting. A user uses different techniques to sort data, viz linear sort, bubble sort, shell sort, etc.
Merging- Combining the data from two data structures (or files) into a single is called merging. This operation improves the competency in searching and ascertains correct data access to the users.
One should begin writing algorithms that implement these data structures once they have a basic understanding of what is data structure and its types. No matter what the data is, its arrangement, and the data structure you choose, influences its usability, security, and integrity. Though each data structure has its pros and cons, it is essential to understand the need of the application and then determine the most suitable data structure that makes the program easy. A basic understanding of data structure, its types, operations, characteristics, the expressions associated with it, and volume is the first milestone in data careers. This session emphasizes on how to theoretically explain data structures.