What Are Data Structures?

Key takeaways

Data structures organize and manage data efficiently, shaping how algorithms work and enabling effective problem‑solving in software development.

• Data structures form the backbone of data handling in software, supporting essential tasks like input, processing, storage, and retrieval.

• Choosing the right data structure directly shapes an algorithm’s efficiency, influencing whether a system runs smoothly or encounters bottlenecks.

• You can start building confidence with data structures by studying the basics, practicing on coding platforms, and applying them in real‑world roles. 

Learn more about the types of data structures and examples of each. Then think about enrolling in UC San Diego’s Data Structures and Algorithms Specialization. In as few as five months, you can learn a range of topics, like how to use core algorithmic methods alongside essential data structures like stacks, queues, trees, and graphs to tackle 100 coding problems commonly asked in high‑tech interviews. 

What are data structures, and why are they important in computing?

Data structures act as foundational frameworks, facilitating data exchange among different segments of an application or across separate software platforms. At their core, data structures provide programmers with a structured approach to data organization and management. The essential functions of data structures include inputting, processing, preserving, and fetching data.

Understanding data structures can help users use computer languages for creative, data-driven problem-solving. Knowledge of this topic may set you apart from candidates without this mastery. Read more: Data Structures Interview Questions

Types of data structures

You can categorize data structures into two primary types: linear and nonlinear. Here's a look at how programmers use linear and nonlinear data structures:

1. Linear data structures

A linear data structure organizes its elements in a sequential order. Each element in this arrangement is directly linked to the subsequent one, except for the last element.

Examples of linear data structures include:

• Arrays: An array holds elements of the same type, accessible by a unique index. Typically, array indexing starts at zero. For instance, in the array fruit = [“apple”, “orange”, “grapes”], you can retrieve the value “apple” using the index fruit[0].

• Stacks: A stack arranges elements sequentially, allowing modifications only to its topmost element. Operating on a "last in, first out (LIFO)” principle, stacks return elements in the sequence opposite to their storage order, giving precedence to the most recently added element

• Queues: Like stacks, queues arrange elements in a sequence. However, in stark contrast to stacks, queues display elements based on their storage sequence, i.e., “first in, first out (FIFO).” 

2. Nonlinear data structures

In a nonlinear data structure, elements aren't placed in a set sequence but adopt a hierarchical arrangement.

Examples of nonlinear data structures include:

• Graph: Graphs comprise nodes (vertices) connected by lines (edges), often used to illustrate systems such as computer networks.

• Tree: Trees feature nodes with keys; parent nodes link to child nodes (or subnodes), all originating from a foundational root node.

• Hash table: Hash tables, or maps, associate keys with values using hash codes for storage. They assist programmers in creating associative arrays, database indexes, and more.

Data structures vs. data types: What's the difference?

Although data structures and types are interrelated, they are also distinct in their usage and function. Base data types, also called primitive data types, are the basis for constructing data structures. Common data types include:

• Integer

• String

• Character

• Boolean

Meanwhile, data structures serve as the foundation for algorithms. Think of an algorithm as a defined series of steps designed to achieve a set goal.

What are data structures and algorithms, and how do they work together in programming? 

While data structures provide the underlying organization and storage mechanism for data, algorithms dictate the specific procedures and logic for performing data computations or operations. Your choice of data structure directly affects an algorithm’s efficiency. An inapt data structure will likely introduce bottlenecks or functional failures.

Example

Consider the following example. You’ve been asked to develop an algorithm for managing lines at a bank. You can choose from one of two data structures:

1. Stack (last-in-first-out structure where the last element added is removed first)

2. Queue (first-in-first-out structure where the first element added is the first one to be removed)

Considering the objective of your algorithm is to serve customers based on their arrival order, which data structure would you choose?

If you were to use a stack to simulate the line at the bank, the last customer to arrive would be the first to receive service, which differs from the expected behavior in a typical system. On the other hand, a queue naturally supports the intended behavior of adding customers to the end of the line and serving them in the order they arrived. As illustrated, data structures determine your algorithm's behavior, underscoring the need for a suitable choice based on task requirements.

Algorithm vs. program: How do they differ?

A program written in programming languages like Python or Java provides computers with explicit task directives. On the contrary, an algorithm defines a methodical strategy, articulated in plain language or programming code, for solving a specific problem.

Put simply, while algorithms and programs both rely on instruction sequences, a program is the practical enactment of an algorithm.

Did you know? Over the years, Google has developed a suite of algorithms, spanning from early spell-check tools to sophisticated AI models, all aimed at optimizing the Google search experience. While each algorithm serves its purpose, today’s results are increasingly shaped by Google’s newer Gemini AI model and upgraded information‑quality systems, which now play a central role in how “Search” understands queries and ranks content. [1].

Learning data structures: How to begin?

To develop algorithms effectively, you need to have a solid understanding of data structures. Below are some tips you can follow to begin gaining a deeper understanding of the intricacies of data structures:

1. Study the basics.

Familiarize yourself with various data structures, such as arrays, trees, and graphs. Similarly, explore different algorithm types, including search algorithms, recursive algorithms, backtracking algorithms, and more. 

2. Use coding platforms.

Like anything else, the key to mastering data structures and algorithms is to practice using them. Begin with straightforward challenges on coding platforms like HackerRank to practice implementing elementary algorithms and data structures. Gradually challenge yourself with more complex problems to refine your skills.

3. Seek industry roles.

Consider opportunities in software engineering or related fields where data structures and algorithms are integral. Internships or entry-level positions can provide firsthand experience applying algorithms to real-world problems.

Build your future career skills with our free resources

Join Career Chat on LinkedIn to stay current with the latest trends in your career field. You can also continue your learning journey with algorithms and programming with our other free digital resources:

• Watch on YouTube: How Long Does It Really Take to Learn Python?

• Bookmark for later: The Ultimate Course on Problem-Solving: Professor Sriram Sankaranarayanan Talks Algorithms

• Take the quiz: Which Python Course Should You Take? Find Out in 1 Minute

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