I love pulling back the curtain on how computers work. I want to go from thinking "that's magic" to "that's unbelievably clever but now I understand how it works." Each time I am able to do this feels like a hard-won but therefore meaningful step toward understanding. I want others to experience this empowering shift. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
I wrote
Algorithmic Thinking: A Problem-Based Introduction
I’ve probably spent more time with this book than with any other technical book. It’s one of those books where you can get as much out of it as you like by revisiting the material at increasing levels of depth. I appreciate the conversational but rigorous tone, the solved examples, the false starts, the intuition that the authors build, and the applications of algorithm design to realistic problems. The Maximum Flow chapter is not to be missed.
Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age.
How do we know whether an algorithm is correct? While intuition is helpful, for tricky algorithms nothing beats the formal proof. But I don’t want a proof for proof’s sake: I want it to deepen my understanding of the algorithm. The proofs in this book series are the best I’ve seen: they are self-contained, described step by step, and serve to sharpen your understanding of what the algorithm is really doing. Couple that fact with the self-check questions, exercises with solutions, and associated video lectures, and what we have here is a wonderful resource for the motivated algorithms learner.
Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The exposition is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. Part 1 of the book series covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the…
Many of my favourite algorithms books give short shrift to designing APIs for the algorithms and data structures that they present. The Sedgewick and Wayne book, by contrast, goes all in on an object-oriented API design. This is my book choice for Java programmers and those interested in larger program design considerations. Clear your calendar: each chapter here is massive, but I think the time investment is worth it. I especially like the chapter that shows how to tune classic algorithms for realizing speedups when working with strings.
This fourth edition of Robert Sedgewick and Kevin Wayne's Algorithms is the leading textbook on algorithms today and is widely used in colleges and universities worldwide. This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing--including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.
The algorithms in this book represent a body of knowledge developed…
This is the book that started it all for me… and I think it holds up just fine today. I see value in confronting the old Pascal code every so often: it’s a reminder of how little we need in order to make our algorithms fast, and how much is happening behind the scenes by our modern programming languages. To this day this book has some of my favourite presentations of Dijkstra’s Algorithm and sorting.
The authors' treatment of data structures in Data Structures and Algorithms is unified by an informal notion of "abstract data types," allowing readers to compare different implementations of the same concept. Algorithm design techniques are also stressed and basic algorithm analysis is covered. Most of the programs are written in Pascal.
For an overview book that focuses on intuition—a book that is intentionally designed to evade formality—to make my list, it has to be really, really good. This one is. I appreciate the inclusion of real code in multiple programming languages and the step-by-step traces of algorithms. I appreciate the care taken with the Big O material and the way that abstract data types are introduced. This is one of very few books whose recursion material I like—the ‘napkin’ approach to recursion is wonderfully done.
If you thought that data structures and algorithms were all just theory, you're missing out on what they can do for your code. Learn to use Big O Notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. This new edition features practice exercises in every chapter, and new chapters on topics such as dynamic programming and heaps and tries. Get the hands-on info you…
Knowing how to design algorithms will take you from being a good programmer to being a great programmer. Algorithmic Thinkingwill teach you how to design your own rocket-fast, correct algorithms. Not interested in wading through proofs and math? Not interested in pseudocode that you can’t run? Not interested in seeing the same examples that you’ve seen in all of the other books? Good—there’s none of that here. You’ll rigorously learn all of the heavyweights that you need to know: hash tables, recursion, dynamic programming, trees, graphs, heaps, union-find, and more. You’ll learn it all in the context of solving programming puzzles. You’ll learn it all from someone who has dedicated their career to helping students learn. It is time for you to finally and truly learn this stuff.
