What Are Data Structures?

Just as a toolbox helps organize tools for easy access, a data structure organizes data for efficient storage and retrieval in computer programs. If you’re starting a career in computer science or data science, proficiency in data structures can enhance your ability to craft robust algorithms.

Read on to learn about the different types of data structures, including their functionalities and typical usage scenarios.

Read more: How to Learn Data Structures and Algorithms: A Basic Guide

Why are data structures important in computing?

Data structures act as foundational frameworks, facilitating data exchange among different segments of an application or across separate software platforms. At its 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.

Types of data structures

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

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

Examples of non-linear 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, assisting programmers in creating associative arrays, database indexes, and more.

Read more: Types of Data Structures

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

Although data types and structures 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. 

Read more: Understanding Different Types of Data

Interplay of data structures and algorithms 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 who arrived would be the first to receive service, which differs from the expected behavior for a typical system. A queue, on the other hand, 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, more advanced models like the 2015-debuted RankBrain, among others, take the lead in shaping search results today [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.

Learn more about data structures with Coursera.

Develop your coding skills with the Foundations of Data Structures and Algorithms Specialization on Coursera. Offered by the University of Colorado Boulder, this Specialization outlines the foundational elements of data structures and algorithms, emphasizing their significance in data science practices.