Data is one of the most powerful tools available to any business or organization that wants to not only survive but rise to the top in today’s competitive and challenging world. The more information available, the more options and better solutions to problems and obstacles open up.

However, this data brings some hefty demands, including a need to keep the information organized and easily accessible. All the data in the world won’t help a business if it can’t reach the data and turn it into an actionable asset.

This dilemma brings us to the answer of a common question — What is data structure? This article will define data structures, explore the different types of data structures, data structure classification, and how data structures are applied. We’ll even delve into concepts like the linear data structure and the nonlinear data structure. We’ll cover resources like data structure interview questions, perfect for people applying for a related position.

Let’s dive right into the world of data structures and algorithms!

Post Graduate Program In Data Science

The Ultimate Ticket To Top Data Science Job RolesExplore Course
Post Graduate Program In Data Science

Well, What is Data Structure, Anyway?

Before defining data structures, let’s back up a little and ask, “What is data?” Here’s a quick answer: Data is information optimized for processing and movement, facts and figures stored on computers.

Data structures are a specific way of organizing data in a specialized format on a computer so that the information can be organized, processed, stored, and retrieved quickly and effectively. They are a means of handling information, rendering the data for easy use.

Every application, piece of software, or programs foundation consists of two components: algorithms and data. Data is information, and algorithms are rules and instructions that turn the data into something useful to programming.

Put another way, remember these two simple equations:

Related data + Permissible operations on the data = Data Structures

Data structures + Algorithms = Programs

Also Read: What Is An Algorithm? Characteristics, Types and How to write it

Data Types and Their Relationship With Data Structures

To answer the question of what is data structure, there are three basic data types to understand.

  • Abstract.

Abstract data is defined by how it behaves. This type encompasses graphs, queues, stacks, and sets.

  • Composite (or Compound).

Composite data comprises combined primitive data types and includes arrays, classes, records, strings, and structs. They may also consist of other composite types.

  • Primitive.

Primitive data is classified as basic data and consists of Boolean, characters, integers, pointers, and fixed- and floating-point numbers.

These data types are the building blocks of data structures. Data types tell the interpreter or the computer how the programmer plans on using the data. Furthermore, data analysts can choose from different data structure classifications. The trick is to select the structure best suited for your needs and situation.

FREE Data Science With Python Course

Start Learning Data Science with Python for FREEStart Learning
FREE Data Science With Python Course

What Are the Classifications of Data Structure?

What is data structure? Good question! It has so many definitions and characteristics, it’s easy to get confused and overwhelmed with the terminology. There are different types and classifications of data structures and the data itself, as we've just seen. This volume of information brings even more questions. What is a linked list? What is a linear data structure? What is data structure???!!

Let’s try to make sense of data structures by looking at the classifications. There are three main data structure classifications, each consisting of a pair of characteristics.

  • Linear and Nonlinear.

Linear structures arrange data in a linear sequence, such as found in an array, list, or queue. In nonlinear structures, the data doesn’t form a sequence but instead connects to two or more information items, like in a tree or graph.

  • Static and Dynamic.

As the term implies, static structures consist of fixed, permanent structures and sizes at compile time. The array reserves a set amount of reserve memory set up by the programmer ahead of time. Dynamic structures feature non-fixed memory capacities, shrinking or expanding as required by the program and its execution requirements. Additionally, the location of the associated memory can change.

  • Homogenous and Non-Homogenous.

Homogenous data structures consist of the same data element type, like element collections found in an array. In non-homogenous structures, the data don’t have to be the same type, such as structures.

The Different Data Structure Types

So far, we have touched on data types and data structure classifications. Our walk through the many elements of data structures continues with a look at the different types of data structures.

  • Array.

Arrays are collections of data items that are of the same type, stored together in adjoining memory locations. Each data item is known as an “element.” Arrays are the most basic, fundamental data structure. Aspiring Data Scientists should master array construction before moving on to other structures such as queues or stacks.

  • Graphs.

Graphs are a nonlinear pictorial representation of element sets. Graphs consist of finite node sets, also called vertices, connected by links, alternately called edges. Trees, mentioned below, are a graph variation, except the latter has no rules governing how the nodes connect.

  • Hash Tables.

Hash tables, also called hash maps, can be used as either a linear or nonlinear data structure, though they favor the former. This structure is normally built using arrays. Hash tables map keys to values. For example, every book in a library has a unique number assigned to it that facilitates looking up information about the book, like who has checked it out, its current availability, etc. The books in the library are hashed to a unique number.

  • Linked List.

Linked lists store item collections in a linear order. Each element in a linked list contains a data item and a link, or reference, to the subsequent item on the same list.

  • Stack.

Stacks store collections of items in a linear order and are used when applying the operations. For example, the order could be “first in, first out” (FIFO) or “last in, first out” (LIFO).

The Ultimate Data Science Job Guarantee Program

6 Month Data Science Course With a Job GuaranteeJoin Today
The Ultimate Data Science Job Guarantee Program
  • Queue.

Queues store item collections sequentially like stacks, but the operation order must be “first in, first out” only. Queues are linear lists.

Also Read: Queue Implementation Using Array

  • Tree.

Trees store item collections in an abstract hierarchy. They are multilevel data structures that use nodes. The bottom nodes are called “leaf nodes,” while the topmost node is known as the “root node.” Each node has pointers that point to adjacent nodes.

  • Trie.

Not to be confused with a Tree, Tries are data structures that store strings like data items and are placed in a visual graph. Tries are also called keyword trees or prefix trees. Whenever you use a search engine and receive autosuggestions, you’re witnessing the trie data structure in action.

Why Are Data Structures Useful?

One of the most important things to learn when you seek the answer to your question — what is data structure? Why is data structure useful?

Data structures offer many advantages to IT-related processes, especially as applications get more complex and the amount of existing data keeps growing. Here are some reasons why data structures are essential.

  • They facilitate greater processing speeds. Large amounts of data require faster processing, and data structures help organize the data into forms that are easier to work with and process.
  • They make it easier to search for data. Data structures organize information into workable forms that are easier to conduct required searches for.
  • They are reusable. Once you implement a given data structure, it can be used anywhere. There is no need to make a new structure. This function saves time and resources.
  • They make it easy to handle multiple requests. You rarely find a single user accessing a database. Instead, it’s common to have hundreds, if not thousands, of users searching and interacting with a database. Data structures arrange information so that users don’t have to search every item — they can instantly search just the required data.

What is Data Structure and Its Applications?

Data structures have many applications, such as:

  • Data Storage.

Data structures facilitate efficient data persistence, like specifying attribute collections and corresponding structures used in database management systems to store records.

  • Data Exchange.

Organized information, defined by data structures, can be shared between applications like TCP/IP packets.

  • Resource and Service Management.

Data structures such as linked lists can enable core operating systems resources and services to perform functions like file directory management, memory allocation, and processing scheduling queues.

  • Scalability.

Big data applications rely on data structures to manage and allocate data storage across many distributed storage locations. This function guarantees scalability and high performance.

Fasttrack your data science career with Simplilearn's unique 6-month Data Science Job Guarantee program and grab your dream job in top companies within 180 days of graduation.

Have You Considered Data Science as a Career?

Data is the fuel that powers today’s engines of commerce, and data scientists are in high demand.  There has been a 650% increase in the demand for data scientists since 2012, and the trend isn’t letting up. The data scientist profession continues to be one of the hottest IT careers today, and now you can join in on this exciting vocation.

Simplilearn has a Post Graduate Program in Data Science course that trains you in everything you need to secure the ideal data scientist position. The data science bootcamp program is perfect for all IT professionals, dealing with job-critical topics such as R, Python programming, machine learning algorithms, NLP concepts, and data visualization with Tableau. These topics and more are covered in great detail through our interactive learning model, featuring live sessions by global practitioners, practical labs, IBM Hackathons, and industry projects.

Conducted in partnership with Purdue University and in collaboration with IBM, this bootcamp is the #1 ranked Post Graduate in Data Science program by ET. You even get masterclasses by Purdue faculty and IBM experts, and “Ask Me Anything” sessions by IBM.

According to Indeed, United States-based Data Scientists earn a yearly average of USD 120,334. Data Scientists in India, Payscale reports, earn an annual average of ₹822,895.

Take advantage of Simplilearn’s courses and get your start on a road that promises a long and prosperous career. Sign up today! 

About the Author


Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.