Types of Data Structures

As a professional in a data-driven field, you can use data structures to manage data so it can be efficiently searched, manipulated, and interpreted. By choosing the right data structure, you can change how you store and process data, greatly enhancing the type of operations you can perform with it. 

Use this guide to explore the basics of data structures and how to choose the right one to improve your performance and efficiency with data-related tasks. You can also learn about different roles that use data structures and their average salaries.   

What are data structures?

Data structures represent the organization and storage of data within computers. You can arrange your data in many ways (using different types of mathematical models), each of which organizes and stores it in a unique format within your computer’s memory. Your choice will affect how fast and easily you can perform tasks with the data. For example, if you need to search through sorted data quickly, you need a data structure that keeps data in order. When choosing a data structure, think about three things: 

1. How do you want to arrange your data?

2. Do you need a way to locate different pieces quickly, such as a data index?

3. Which operations do you need to edit your data? Examples include adding, finding, changing, or deleting it.

The design of any program that deals with data starts with picking the right data structure. Each type of data structure must make certain trade-offs, balancing memory, speed, and function. When choosing a data structure, consider what your highest priority is.

Read more: What Is Data Analysis? (With Examples)

What are data structures used for?

Data structures are fundamental to computer science and data science. They are behind the scenes in many types of systems, from databases and websites to apps that use machine learning. Essentially, any program that handles data relies on data structures to work efficiently and effectively.

Professionals in computer science fields use data structures to store and manage large volumes of data for a wide variety of tasks. Depending on your need, you might choose data structures suited for tasks such as:

• Sorting information

• Stacking information in order of importance

• Storing data effectively during latent periods

• Representing structural relationships of data

• Assessing graphical representations of data networks

• Building association arrays

• Removing and inserting information quickly

Read more: 7 In-Demand Data Analyst Skills to Get You Hired

Types of data structures

Data structures can be divided into several types, including linear and nonlinear structures. Within each type, subtypes suit different purposes and provide unique ways of arranging and linking data. 

1. Linear data structures

Linear data structures organize data in sequence, with each element arranged on a single level and connected to elements on either side. In these structures, you perform operations such as insertion or deletion in a linear sequence.

2. Linked lists

A linked list is a sequence of elements where each one contains reference information for the element next to it. With linked lists, you can efficiently insert and delete elements, easily adjusting the size of your list.

3. Arrays 

An array is a group of data elements arranged in adjacent memory locations. The index gives the direct address of each element, making arrays a highly efficient data structure for accessing different data pieces. Arrays are common across computer science functions as a convenient way to store accessible data.

4. Stacks 

Stack structures follow the last in, first out (LIFO) principle. The addition (push) and removal (pop) of elements happen only at one end, referred to as the top of the stack. For example, if you enter a new element, it appears on top. This element addition is a “push,” as it pushes all the previous elements down. If you then remove the elements, this is a “pop.” One of the most common uses of this algorithm is the “undo” function in text editors.

5. Queue

A queue structure uses the first in, first out (FIFO) principle, meaning that you add elements from the back (enqueue) and remove them from the front (dequeue).  

6. Nonlinear data structures

Nonlinear data structures do not organize data sequentially. Instead, they form a hierarchical arrangement where one element connects to one or more elements, leading to a branching structure. 

7. Graphs

Graphs are fundamental types of nonlinear data structures. A graph is a collection of nodes connected by edges. It can represent networks, such as social networks or transportation systems. However, graphs are often chosen to represent networks of information, such as social media relationships or geographical maps. 

8. Trees

A tree is a graph structure with a hierarchy of nodes. The top node is the “root,” and the descendants of this node are “children.” Nodes with no children are “leaves.” You can use trees in various applications, including hierarchical data representation, databases, and searching algorithms.

9. Binary trees

Binary trees are a type of tree where nodes have zero to two children. Binary trees are widely used in searching and sorting algorithms, as well as building more complex structures like binary search trees (BSTs), where each node follows a specific order.

10. Tries

Another notable subset of trees is the “trie,” a tree algorithm designed for text data. It is usually used for applications such as spell-checking, finding words in a dictionary, or keyword prediction. 

Searching and sorting algorithms

Searching and sorting algorithms are algorithms applied to data structures. Being able to sort and then search your data is a fundamental step before you can process and interpret your data.

Data sorting algorithms 

Once you have your data in a list or array, you can apply sorting algorithms to rearrange it. Sorting algorithms, such as quicksort, merge sort, and insertion sort, organize data within structures, making it easier for searching algorithms to find data efficiently.

Data search algorithms

You use data search algorithms to find elements stored within your data structure. For data stored linearly, using linear or binary searches is best. For nonlinear data structures, opting for graph traversal algorithms is ideal.

Linear and binary search algorithms

Linear search algorithms search through data sequentially, starting with the first element and moving to the next element in a line until finding the target. With binary search, the algorithms continually divide the data in half until it reaches the target. For example, if you had a list of one to 10 and were searching for a three, the algorithm would first divide the 10 digits into one to five and six to 10, realizing the three fit into the one to five category. The algorithm would then divide one to 2.5 and 2.5 to five, realizing the three is in the 2.5 to five category, and so on.

Graph traversal algorithms

Graph traversal algorithms provide a way to move throughout nodes within a graph and find needed information. When using a depth-first search (DFS), the algorithm explores a certain path of the graph as far as it can before turning around and going back up the path. This method is more common to find nodes farther away from the starting point.

When using a breadth-first search (BFS), the algorithm explores all nodes at a certain depth before moving on to the next level. This method is more common to find nodes closer to the given source.

Who uses data structures? 

Data structures form the foundation of many computing tasks, including computer networking, computer programming, and data analysis. If you work in an industry that deals with data, you could benefit from a better understanding of data structures. The following careers are top choices for people who want to work in a data-driven profession.

1. Data scientists

Average annual base salary (US): $129,794 [1]

Data scientists work in many industries and often perform tasks related to data mining, management, storage, and processing. To succeed in this career, you'll need a strong foundation in qualitative and quantitative data analysis, as one of your main job responsibilities will be to extract insights from your data.

Read more: What Is a Data Scientist? Salary, Skills, and How to Become One

2. Business analysts

Average annual base salary (US): $87,858 [2]

As a business analyst, you will perform many of the tasks of a data analyst—such as collecting and analyzing data—in a business context. You will take data and build specific types of models to represent the story of the data, using these insights to help businesses make data-driven decisions.

3. Software engineers

Average annual base salary (US): $119,063 [3]

Software engineers combine software development with engineering principles. They support all stages of building software systems. To be in this position, you’ll need to understand computer science principles and their practical application, including data structures and algorithms.

Read more: What Does a Software Engineer Do?

4. Software developers

Average annual base salary (US): $106,054 [4] 

Software developers use data structures extensively to solve problems by designing software solutions. Understanding data structures allows you to choose the most appropriate data handling methods for your projects as a software developer, directly impacting the performance of software applications.

Read more: What Does a Software Developer Do? Career Overview + Outlook

How to start working with data

Consider the following steps to begin learning about data structures. This will help you establish a starting point for more advanced studies.

• Learn the basics: Start with reviewing the fundamentals of data structures, such as linear and nonlinear structures. 

• Take online courses: Platforms like Coursera host courses on a wide array of data topics. These courses allow you to learn at your own pace in any environment, making them a flexible and accessible option.

• Practice coding: Practice makes perfect. Coding remains an in-demand skill, and cultivating it can help you improve your problem-solving abilities.

• Build projects: Applying what you’ve learned in real-world projects can solidify your understanding and give you practical experience. Plus, you can use these projects to showcase your skills when looking for jobs.

Learn more with Coursera.

Ready to learn more about data? On Coursera, you can customize your learning journey by choosing between courses, Specializations, and Professional Certificates designed for different backgrounds and experience levels. For beginners, consider taking Introduction to Data Analytics to take your first steps toward a career in data-driven industries. Upon completion, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.