# What Are Data Structures and Algorithms?

Sakshi Gupta | 5 minute read | July 8, 2020

Data structures and algorithms are a critical part of a computer science education, though not something that most bootcamps graduates or self-taught people will know very well.

This guide will serve as an introduction to data structures and algorithms, especially for those who do not have formal education in the space.

Mastering data structures and algorithms are key to becoming a successful software engineer. Learn more about data structures and algorithms in this comprehensive guide.

Looking to get into software engineering? Check out Springboard’s comprehensive guide on how to become a software engineer.

## What Are Data Structures and Algorithms?

A data structure is a method of organizing data in a virtual system. Think of sequences of numbers, or tables of data: these are both well-defined data structures. An algorithm is a sequence of steps executed by a computer that takes an input and transforms it into a target output.

Together, data structures and algorithms combine and allow programmers to build whatever computer programs they’d like. Deep study into data structures and algorithms ensures well-optimized and efficient code.

## How Do Data Structures and Algorithms Work Together?

There are many algorithms for different purposes. They interact with different data structures in the same computational complexity scale. Think of algorithms as dynamic underlying pieces that interact with static data structures.

The way data is expressed in code is flexible. Once you understand how algorithms are built, you can generalize across different programming languages. In a sense, it’s a bit like knowing how a related family of languages work syntactically. Once you glimpse the fundamental rules behind programming languages and their organizing principles, you can more easily switch between the different languages and learn each faster.

## Common Data Structures and Algorithms

Common data structures you’ll see across different programming languages include:

• Linked lists
• Stacks
• Queues
• Sets
• Maps
• Hash tables
• Search trees

Each of these has its own computational complexity for associated functions like adding items and finding aggregate measures such as the mean for the underlying data structure.

Some common categories of algorithms are:

• Search
• Sorting
• Graph/tree traversing
• Dynamic programming
• Hashing and regex (string pattern matching)

## How Do You Learn Data Structures and Algorithms?

It’s important to learn data structures and algorithms properly so you can understand the organizing principles behind web development and programming work.

Follow these steps to ensure your learning is as efficient as your algorithms will be.

1. Gradually move beyond HTML/CSS to a programming language. Python is good because it’s versatile and can be used for many programming paradigms, and has more elegant syntax than JavaScript. Eventually, you’ll work towards defining your own data structures and algorithms.
2. Get familiar with computational complexity. In particular, Big O notation and the different scales of time and space that represent the worst-case scenarios for your algorithms from input to output, from linear, polynomial, exponential, to logarithmic time scales. These scales will have dramatic differences in the performance and expected computation times of your algorithms. Something that might be logarithmic might scale decently well with large data sets and inputs, while something that is exponential may never finish in time.
3. Understand different data structures and algorithm types. Read through basic data structure and algorithm types to get a better feel for the subject.
4. Practice, practice, practice. Practice implementing algorithmic principles and actual algorithms and data structures with different exercises. Build your own programs.
5. Get on-the-job training. Get a job in software engineering or a role where data structures and algorithms are implemented in order to best exercise your new knowledge.

## Data Structures and Algorithms in Python

This guide will walk you through data structures and algorithms in Python and their different implementations. It’ll walk through tangible examples of Python’s built-in data structures, as well as examples of Python algorithms from search to sorting.

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## Data Structures and Algorithms in JavaScript

This guide will walk you through data structures and algorithms in JavaScript, which is mostly used for web development and certain software engineering aspects.

Understanding data structures and algorithms in JavaScript will allow you to branch out and generalize your learnings to a programming language known for having a bit more of a difficult syntax than Python.

## Interview Questions on Data Structures and Algorithms

Top companies and others looking to hire web developers and software engineers might ask data structures and algorithm interview questions in order to try to qualify and sift through large pools of candidates.

One example is the now-infamous whiteboard interviews, where interview candidates are asked to write out an algorithm and how it interacts with a data structure by hand, without the use of a computer.

Anyone looking for a software engineering job should be ready for these interview situations and be ready to understand data structures and algorithms in order to be prepared.

## Resources to Learn Data Structures and Algorithms

Here is a list of resources to help you learn data structures and algorithms.

## How Does Springboard Help You Master Data Structures and Algorithms?

Want to practice and implement data structures and algorithms? Springboard will help you master Python and JavaScript so you can implement different data structures and algorithms. You can learn these data structures and algorithms online and at your own pace.

In Springboard’s Software Engineering Career Track, you will learn:

• The theory behind data structures and algorithms
• How to build and work with data structures in Python
• The relevance of Big O notation and how to use it to create beautiful, efficient code
• Common types of data structures you’ll see working as a software developer
• The details and precise implementation behind algorithms from search to sorting

All this is done with Springboard’s proven method of instruction, which includes:

• 1:1 mentorship with an industry expert
• Curriculum built by leading instructor Colt Steele
• Weekly office hours
• Personalized career coaching and interview prep

Springboard’s Software Engineering Career Track is the preferred choice of aspiring software engineers looking for a dedicated team to guarantee their career success. It also comes with a job guarantee: every graduate has six months to secure an industry role in software development, or their tuition is refunded in full.

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#### About Sakshi Gupta

Sakshi is a Senior Associate Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer and has experience working in the Indian and US markets.