A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills 1st Edition

I have this book, as well as "Grokking Algorithms," "Algorithms Unlocked" (by Thomas Cormen of CLRS), "Algorithms 4th ed" PDF by Sedgwick/Wayne, and a copy of the big fat "Introduction to Algorithms" (aka "CLRS", used in many undergrad CS Algorithms courses) on my book shelf.Jay Wengrow's "Common Sense Guide to Data Structures and Algorithms" lives up to its title.This is by far the easiest of the bunch to read. It has, in my opinion, the clearest explanations for a non-math, non-CS graduate. The algorithms and data structures, their purposes and pitfalls, and simple comparisons of when/why/how to use them all come together in a short, informative, and entertaining read.The step-by-step walkthroughs and code examples I'd say put it a step beyond "Grokking Algorithms," which takes a similar "fun and illustrated" approach, but isn't quite as detailed. Having read that first might have also helped me understand this one a bit better - it's very similar material. Wengrow's book nicely fills in some of the gaps left by "Grokking" by deliberately walking through *every* step. He explains the *entire* process of each sorting algorithm, even when it seems somewhat tedious to do so. It remains concise despite the detail & repetition, which make the code examples easier to follow.I bought "Algorithms Unlocked" several years ago as an "easy" intro to algorithmsThat one covers many of the same topics, but is more dense 'academic' text with screwy notation and no code.I was bored to death by it, and annoyed by Cormen's use of 1-based array indices that needlessly complicated the formulas.This book by Jay Wengrow succeeds where that one left me wanting."Common Sense" is an apt description; and the bits that aren't "common" he explains in an understandable, practical way.After reading (and re-reading) this book with its clear, layman's explanations and examples to help cement things conceptually, I think I'll give Cormen's "easy" book another chance, and then move on to tackling the more complicated "real" textbooks (likely with a good deal of math-class refreshers in the process).

The author's approach in this book of relying heavily on diagrams in explaining concepts worked much better for me than in other algorithm-centric books that didn't do so. As many times as I've read bout Big O Notation elsewhere, I really feel that the author was able to explain it succinctly, but thoroughly enough that it made sense and provided the foundation needed to understand, in a general sense, how/why one data structure and/or algorithm is measured over another. The other key takeaway for me was that the author explained the common forms that one might see a Big O Notation come in so you could also compare the most desirable to least desirable Big O Notations for a given data structure or algorithm. As basic as that may seem, that really is a fundamental that you can't gloss over or only partly understand if you really want to dive into this material and improve yourself. I read the book across the course of a few days, so I think this is one of those books that you could finish in a weekend or two weekends. It's not a heavy read, in other words. I consider that a plus because other algorithm books that I've read are quite in-depth and it's hard to get a lay of the land (if that's what you want) when you have to commit a couple of weeks to work through a book. This book doesn't force you into learning a programming language just to understand the algorithm examples that were written in Ruby, JavaScript, and/or Python. If you're fairly familiar with programming in those languages or something similar such as PHP or Java, you should be ok. By the way, I bought the Kindle version of this book and it formatted nicely -- text and diagrams. It's sad that I should have to call that out as it should be the minimum standard in any Kindle book, but alas, other Kindle books don't get their diagrams to display correctly. Now that I've got a good handle on the basic of basics for Big O Notation and a few compare/contrast of some common algorithms, I'm going to return to my other algorithm books that go into the material much more deeply (e.g "Algorithms (4th Edition)" by Robert Sedgewick).

Jay provides the best, most easy to understand explanations for data structures and algorithms. I found it hard enough to find resources to get started with programming, not knowing what exactly to be looking for, and this book took away all of that hassle. If you dream of one day working for one of the top tech giants like Google, Facebook or Amazon, this is a great place to start. After reading this book, keep it in your library as a resource to refer back to. I was constantly going back to this book when studying for my interviews.

I read this for my DSA 1 course and found it helpful; a lot of the book was ruby which was new to me but I was able to grasp the concepts still. I would recommend this to other students looking for a text to get started on DSA.

I ordered this book so I can use it as a guide for my daughter. According to the review it looked good so I went ahead and ordered. When I received it, I noticed that there are countless print errors (I don't know what else to call it) with many diagrams used in the book. It is NOT a typo I am talking about, many of the letter and numbers in the diagram show as empty rectangles. I can confirm it by comparing my pictures against "Look Inside" view of the book - I am including pp7, 8 and 9 of the copy I received.I returned and requested another copy but same exact problem exists. Amazon should not really be selling the version with so many print errors.I hope others somehow receive the correct version. Amazon (or author), let me know once you have a copy that does NOT have the printing issue. I will be willing to check it and confirm if you send me a sample but I am not paying for this.

Este é o melhor livro que eu conheço para introdução ao tema de algoritmos e estruturas de dados. Possui uma didática excelente e ótima seleção de conteúdo. Progride lentamente, passando por diferentes estruturas e algoritmos, enquanto apresenta como fazer a análise de complexidade de cada um deles, sem se perder em expressões matemáticas.Peca um pouco na parte de recursão, mas ao que parece esta parte foi revista na segunda edição que está sendo elaborada. A parte de grafos também deixa um pouco a desejar. Por exemplo, apresenta bases de dados com grafos, um espaço que poderia ser dedicado a outras questões mais importantes, como conectividade de grafos. Algumas definições importantes e simples, como caminho, não são explicitamente apresentadas.O livro seria ainda melhor se fosse em apenas uma linguagem, ou então se tivesse um apêndice com todos os códigos em todas as linguagens usadas no livro (ou, quem sabe, no website). Para quem tem preferência por Python, o livro "Problem Solving with Algorithms and Data Structures using Python" é um excelente complemento a este livro.

This is the book I wish I had when I was a student. Having taken a handful of CS classes in university, this book provided better explanations than any professor I had.

I am studying an Artificial Intelligence degree at University so bought this to prepare for Algorithms with no prior experience - especially since I have only a GCSE level of Maths. Luckily, this book explains simple concepts slowly and ramps up at an appropriate pace. Most of the book could be understood by someone who has never actually seen a computer before, such is the quality of the step by step explanations.I would heartily recommend this to anyone with any level of knowledge of Algorithms who wants to learn more.

Before reading any algorithm books or if not understanding any other books go for this book first.

This book is a delightful read, very easy and simple to get through giving you a very good understanding of data structures and algorithms