Master the JavaScript interview with expertly curated questions and solutions. Strategies developed by FAANG engineers help you prep faster, boost confidence, and refine your problem-solving skills.

Master the JavaScript interview by practicing 20 carefully curated interview questions and solutions. These problems will provide the tools you need to tackle any question you find in an interview. Allow ~1 hour per question.

Most questions have multiple solutions presented. All solutions are thoroughly explained and time and space complexity are discussed. A passing familiarity with these ideas is expected. You'll learn how to tackle different types of problems so you can proceed with confidence in your interviews.

These questions go beyond the common problems that we've all seen. FizzBuzz and bubble sort? We start past those.

We'll discuss how to refine algorithms. We'll go over clever, advanced ways of manipulating data that interviewers are looking for. You'll walk away more skilled and confident in your problem solving and interviewing abilities.

This course is taught in ES2015.

Master the JavaScript Interview

# Welcome

### Goals

This course focuses on teaching problem-solving strategies geared towards helping you expertly answer many software engineering interview questions.

Almost all of these problems are variations on questions I have been asked or that others I know have been asked in interviews. They’ve been curated in a way that will allow us to go over several problem-solving strategies.

### The Problems

Every problem comes with a solution and a detailed description of the strategy employed. Most come with two or three different solutions with each discussed in detail compared to the others.

Typically we'll start with a brute-force algorithm that solves the problem. We'll then refine it until we can optimize time complexity, space complexity, or both.

We'll calculate and discuss time and space complexity for every single solution as well.

### What You'll Get

By the end of the course, you’ll have mastered several algorithmic strategies that can be applied to a wide range of problems. Our discussions will teach you how to approach different types of problems, allowing you to start in the right direction and find the answer during actual interviews.

We’ll teach ways to critically analyze the algorithms we come up with and find ways to make them more efficient. We’ll fine-tune algorithms to reduce the amount of time they take or the space they need. This is exactly what an interviewer is looking for.

# Format

The format of each problem is similar. We start by describing the problem and constraints, giving examples when needed. Some problems include hints to get you started.

There is then an empty solution box for you to try solving it yourself. It'll look something like this. Complete the function and hit the `Test` button.

function add(a, b) {
    // Your code here
}

The difference is that we'll leave the function blank. It'll have tests lined up and ready to go to make sure your function works as expected.

After the blank workspace, we'll dive into solutions. We'll start with a brute-force solution and discuss how it works and how efficient it is. If better solutions exist, we work on them after.

### Expectations

- You should have a basic working knowledge of JavaScript and the ability to write meaningful code with it.
- You should be familiar with basic ES2015 concepts such as `let` and `const` declarations, arrow functions, and classes.
- You should have some idea of what the terms *time complexity* and *space complexity* mean. As long as you're aware of what they are, you'll do fine, as we walk through finding them for every problem.

  For an understanding of time and space complexity, feel free to look at the following pages:

  --[Big O Notation](https://www.interviewcake.com/article/java/big-o-notation-time-and-space-complexity)

  -- [What is a plain English explanation of “Big O” notation?
](https://stackoverflow.com/questions/487258/what-is-a-plain-english-explanation-of-big-o-notation)

# Welcome

## Goals

This course focuses on teaching problem-solving strategies geared towards helping you expertly answer many software engineering interview questions.

Almost all of these problems are variations on questions I have been asked or that others I know have been asked in interviews. They’ve been curated in a way that will allow us to go over several problem-solving strategies.

## The Problems

Every problem comes with a solution and a detailed description of the strategy employed. Most come with two or three different solutions with each discussed in detail compared to the others.

Typically we'll start with a brute-force algorithm that solves the problem. We'll then refine it until we can optimize time complexity, space complexity, or both.

We'll calculate and discuss time and space complexity for every single solution as well.

## What You'll Get

By the end of the course, you’ll have mastered several algorithmic strategies that can be applied to a wide range of problems. Our discussions will teach you how to approach different types of problems, allowing you to start in the right direction and find the answer during actual interviews.

We’ll teach ways to critically analyze the algorithms we come up with and find ways to make them more efficient. We’ll fine-tune algorithms to reduce the amount of time they take or the space they need. This is exactly what an interviewer is looking for.

## Expectations

- You should have a basic working knowledge of JavaScript and the ability to write meaningful code with it.
- You should be familiar with basic ES2015 concepts such as `let` and `const` declarations, arrow functions, and classes.
- You should have some idea of what the terms *time complexity* and *space complexity* mean. As long as you're aware of what they are, you'll do fine, as we walk through finding them for every problem.

  For an understanding of time and space complexity, feel free to look at the following pages:

  --[Big O Notation](https://www.interviewcake.com/article/java/big-o-notation-time-and-space-complexity)

  -- [What is a plain English explanation of “Big O” notation?
](https://stackoverflow.com/questions/487258/what-is-a-plain-english-explanation-of-big-o-notation)

Introduction

Strings & Arrays

Advanced Strings & Arrays

Algorithms & Data Structures

JavaScript Mastery

Conclusion

Welcome

Welcome

Goals

The Problems

What You’ll Get

Format

Expectations