# The 5 Best Books on Algorithms for Mastering the Code Interview

To master the code interview for top companies like Amazon, Microsoft, Google, and Apple, you can select one of the best books on algorithms to be your companion — to teach you how to build, test, and optimize algorithms based on common algorithms. These skills are essential to passing a traditional whiteboard coding interview or simply to becoming a better programmer.

The best books on algorithms gives programmers a common language to talk and think about code. The best books on algorithms also teach us the most important algorithms to plug-and-play or understand algorithm design. With algorithms, we can measure what is effective or less-so. We can have metrics to optimize our code.

Why do code interviews test your knowledge of algorithms? It shows that you are a good programmer and a good thinker. As programmers, we are ultimately problem-solvers, and common algorithms give us the tools to design solutions to our coding problems.

As self-taught programmers or as programmers studying for a coding interview, books can help us reach our goals

**READ THE SERIES ABOUT ALGORITHM BOOKS:**

**Other Books on Code articles about algorithms:**

## Overview of What Makes the Best Books on Algorithms

What makes the best books on algorithms? Good algorithms books do several, but perhaps not all, of the following:

They teach the most important algorithms.

They teach with precision and clarity.

They have good problems and questions for you to work on your own.

They allow you to not just think of algorithms as templates, but allow you to think of and design your own solution based on metrics such as clean code and code efficiency.

Friendly toward self-taught programmers. Not too heavy in mathematics. Does not have a high barrier to entry.

[Bonus] They have an engaging, conversational style and introduce novelty to keep you interested.

If you know me and *Books on Code*, I am a big advocate of bringing *joy* into the learning experience. With a heavy topic like algorithms, the texts themselves tend to be heavy and serious as well, easily clocking in at over 1000 pages of dense text. While texts that are dense and academic are in this list for their valuable content and breadth, I also include some titles to remind you that learning can also be fun.

## Best Books on Algorithms

**Here are the best books on algorithms:**

### Book 1: *Algorithms *by Robert Sedgewick

*Algorithms* is a book that delivers on exactly what you want from an *algorithms* book: clean, simple, straightforward explanations of the most important algorithms. With examples based in Java, the book is well-organized and thorough, reaching nearly 1000 pages.

For more information about what this book is about, look to the introduction of *Algorithms*:

This book is intended to survey the most important computer algorithms to use today, and to teach fundamental techniques to the growing number of people in need of knowing them. […] Before developing our fundamental approach to studying algorithms, we develop data types for stacks, queues, and other low-level abstractions that we use throughout the book. Then we survey fundamental algorithms for sorting, searching, graphs, and strings. The last chapter is an overview placing the rest of the material in the book inn a larger context.

I like this. Why? It puts you in the weeds *first* and saves the theory for later. This is how people learn best. Once we know how to use something, we become hungry for the ‘why,’ but if we get the ‘why’ first, it hardly sticks.

The book is divided into six of the following chapters:

**Chapter 1**covers fundamentals such as primitive data types, loops, arrays, objects, APIs, linked lists, Tilde notation, and memory usage.**Chapter 2**covers sorting algorithms such as mergesort and quicksort.**Chapter 3**covers searching algorithms and data types such as binary search and hash tables.**Chapter 4**covers graph algorithms such as shortest-path algorithms and depth-first search.**Chapter 5**covers algorithms related to streams such as string sort, tries, substring search, data compression, and regular expressions.**Chapter 6**covers topics around context-reliant algorithms such as B-trees, reductions, suffix arrays, and event-driven simulation.

As you may gather from the chapter outline, the book covers “classic methods that have been taught since the 1960s and new methods that have been invented in recent years.” The goal is to reach the widest audience possible with the most important algorithms. To reach that goal, the information is well organized and explained.

If you are a self-taught programmer, I recommend this book if you are serious about supplementing a traditional computer science course curriculum.

### Book 2: *Introduction to Algorithms *(The MIT Press)

*Introduction to Algorithms* is not light for an ‘introduction’. It begins with basics, like defining what ‘pseudocode’ is, but it gets deep. It is not just an algorithm cookbook; it teaches “techniques of algorithm design and analysis so that you can develop algorithms on your own.” This skill is valuable for writing great code — and of course, it’s great for the coding interview.

The book asks good questions and truly sets the intention to help you have a good and correct philosophy about algorithms.

The book is divided into only seven of the following chapters:

Chapter 1 covers foundational concepts such as the role of algorithms and some basic algorithms and thoughts of algorithm development like insertion sort, notations, the divide-and-conquer method and randomized algorithms.

Chapter 2 covers sorting and order statistics such a Quicksort.

Chapter 3 covers data structures such as hash tables and trees.

Chapter 4 covers advanced design and analysis techniques such as dynamic programming and greedy algorithms.

Chapter 5 covers advanced data structures such as B-trees, Fibonacci heaps, and disjoint sets.

Chapter 6 covers graph algorithms such as Dijkstra’s algorithm.

Chapter 7 covers “selected topics” such as multithreaded algorithms, matrix operations, and linear programming.

As you can see from the book’s outline, an ‘introduction’ does not quite cover what this book offers. It goes into ‘advanced’ topics by chapter 4 and it *just keeps going*. As someone who feels comfortable and confident in the ‘data structures & algorithms’ taught in the classroom, this book helped me see how I can vastly improve as a programmer. This book is recommended not just for beginners but for anyone who is serious about improving in this dimension of programming.

### Book 3: *The Algorithm Design Manual*

*The Algorithms Design Manual** *is branded as a reader-friendly guide, which is great for self-taught programmers. The book is designed to take the mystery out of designing algorithms so that you can analyze their efficiency.

This book is about algorithm *design*, as the title says. For example, the introduction of the book states that there are three desirable properties for a good algorithm: *correct*, *efficient*, and *easy to implement*. Memorize this for a programming interview right now.

A book that focuses on design specifically is extremely valuable for a coding interview, since you can’t just memorize important algorithms, but you need to think and reason around them.

This book is great for its conversational writing style. Other books that focus on algorithms design use mathematical formulas to explain this concept. This book is friendly to self-taught programmers.

What content is in this book? Quite a lot. The book is around 700 pages and 19 chapters. The first part of the book, titled “Practical Algorithm Design, covers the following:

Introduction to algorithm design, which covers how to reason about correctness and modeling the problem.

Algorithm analysis

Data structures

Sorting and searching

Graph traversal

Weighted graph algorithms

Combinatorial search and Heuristic methods

Dynamic programming

Intractable problems and approximation algorithms

The rest of the book focuses on a series of problems, grouped into types such as linear equations and combinatorial problems such as sorting and searching.

This book is practical, with lots of problems. Even though the book is more reader friendly than other algorithm design books, the text is still heavy. Be prepared to work.

### Book 4: *Grokking Algorithms*

With charming graphics, *Grokking Algorithms* illustrates common algorithms and data structures that we might learn in a data structures & algorithms class. Some of the concepts covered include Big-O notation, hash tables, trees, quicksort, and recursion — all extremely valuable concepts for writing efficient code and for code interviews.

I highly recommend *Grokking Algorithms* for beginner programmers because the guide is brain friendly. What do I mean by brain friendly? Learning is easier for us when books use techniques to engage us: conversational style, direct questions, breakpoints for reflection, and novelty.

Some of the topics covered in *Grokking Algorithms*:

Binary search

Big O notation

Arrays and linked lists

Selection sort

Recursion

Stacks

Quicksort

Hash tables

Breadth-first search

This book does a fantastic job of explaining complex concepts to complete understanding using few pages but *extremely clear* visual explanations. If you have ever struggled with recursion, just five pages in this book will the concept real quick.

### Book 5: *Cracking the Code Interview*

*Cracking the Code Interview* sets out to do exactly what the title says: crack the code interview. Traditional whiteboard interviews rely on designing algorithms to solve a problem. This book presents 189 algorithm problems and a breakdown of their solutions. The book is massive and intimidating, but it is also smart and well-crafted, considering it was written by someone who conducted interviews at major companies.

Each programming problem opens with a discussion of the core knowledge and strategies to tackle the type of question. Then the book dives into how you break down and solve the problem.

Some of the types of problems covered in this book:

Arrays and Strings

Linked Lists

Stacks and Queues

Trees and Graphs

Bit Manipulation

Math and Logic Puzzles

Object-Oriented Design

Recursion and Dynamic Programming

Sorting and Searching

Scalability and Memory Limits

For a summary of the code interview process described in the article, see the *Books on Code* article *7 Effective Steps to Cracking the Code Interview**.*

## Bonus Book: *The CS Detective*

*The CS Detective: An Algorithmic Tale of Crime, Conspiracy, and Computation** *is a fiction book that teaches computational thinking and search algorithms through a series of stories and illustrations. While the book is not a comprehensive text and the stories do not substitute the depth a textbook provides, this is certainly a fun way to continue to think about algorithms when you are not doing back-breaking studying work.

I recommend getting this book in addition to a more substantial textbook. When you are not studying the textbook, this book can provide a ‘break’ that still allows you to continue to learn and think about algorithms. When I learn new skills, I like the experience to be immersive. This book delivers on that.

This book is just fun. You will enjoy picking up at the computer science puns in all of the characters’ names and in the wacky scenarios. For example, Chapter 2 teaches the concept of exhaustive search. Detective Frank, graduate of Police Algorithms Academy, goes to a “rutted dirt road lined with combination of seedy bars and upscale coffee shops” on Three Bit Lane.

The following illustration demonstrates exhaustive search as Detective Frank tries to find and interrogate Glass Box Billy. With each location Frank visits, the arrow goes to the right:

I include this book as a “Bonus Book,” because while it does not qualify as a complete algorithm book, it is fun and highly recommended!

I hope you enjoyed this article and have found value reading about these best books on algorithms.

Sometimes learning can be lonely between just you and your textbook. If you know me, you know I’m a raving fan of Coursera courses. For structure and accountability, with great instruction and material, I personally love Coursera’s Data Structures & Algorithms specialization from Stanford University. They go over the topics covered in these textbooks — sort, graph search, dynamic programming, and shortest-path problems — but, you know, just not in book form. 😊

Going beyond algorithms — if you are interested in also learning all of your computer science fundamentals in one place, I cannot recommend Coursera’s Fundamentals of Computing by Rice University highly enough. The courses cover “much of the material that first-year Computer Science students take at Rice University.” And being honest — I got far more from this than I did my local university curriculum.

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Hope you have a wonderful day and I will see you next time. 👋😊