Peeling Data Structures and Algorithms: Table of Contents: goo.gl/JFMgiUSample Chapter: goo.gl/n2Hk4iFound Issue? goo.gl/forms/4Gt72YO81IVideos: goo.gl/BcHq74"Data Structures And Algorithms Made Easy: Data Structures and Algorithmic Puzzles" is a book that offers solutions to complex data structures and algorithms. There are multiple solutions for each problem and the book is coded in C/C++, it comes handy as an interview and exam guide for computer scientists.A handy guide of sorts for any computer science professional, Data Structures And Algorithms Made Easy: Data Structures and Algorithmic Puzzles is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. This book serves as guide to prepare for interviews, exams, and campus work. In short, this book offers solutions to various complex data structures and algorithmic problems.
Publication date: 01/13/2017Kindle book details: Kindle Edition, 432 pages
Published by: Pragmatic Bookshelf | Publication date: 08/03/2017Kindle book details: Kindle Edition, 222 pages
This is the eBook version of the printed book. Essential Information about Algorithms and Data Structures A Classic Reference The latest version of Sedgewick’s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades. Broad Coverage Full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing, including fifty algorithms every programmer should know. Completely Revised Code New Java implementations written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. Engages with Applications Algorithms are studied in the context of important scientific, engineering, and commercial applications. Clients and algorithms are expressed in real code, not the pseudo-code found in many other books. Intellectually Stimulating Engages reader interest with clear, concise text, detailed examples with visuals, carefully crafted code, historical and scientific context, and exercises at all levels. A Scientific Approach Develops precise statements about performance, supported by appropriate mathematical models and empirical studies validating those models. Contents Chapter 1: Fundamentals Programming Model Data Abstraction Bags, Stacks, and Queues Analysis of Algorithms Case Study: Union-Find Chapter 2: Sorting Elementary Sorts Mergesort Quicksort Priority Queues Applications Chapter 3: Searching Symbol Tables Binary Search Trees Balanced Search Trees Hash Tables Applications Chapter 4: Graphs Undirected Graphs Directed Graphs Minimum Spanning Trees Shortest Paths Chapter 5: Strings String Sorts Tries Substring Search Regular Expressions Data Compression Chapter 6: Context
Published by: Addison-Wesley Professional | Publication date: 02/21/2011Kindle book details: Kindle Edition, 952 pages
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 master method, randomized algorithms, and several famous algorithms for sorting and selection.
Published by: Soundlikeyourself Publishing, LLC | Publication date: 09/25/2017Kindle book details: Kindle Edition, 952 pages
The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.
Published by: The MIT Press | Publication date: 07/31/2009Kindle book details: Kindle Edition, 1312 pages
If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online.
- Use data structures such as lists and maps, and understand how they work
- Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree
- Analyze code to predict how fast it will run and how much memory it will require
- Write classes that implement the Map interface, using a hash table and binary search tree
- Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results
Published by: O'Reilly Media | Publication date: 07/07/2017Kindle book details: Kindle Edition, 158 pages
A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications.Probabilistic data structures is a common name for data structures based mostly on different hashing techniques. Unlike regular (or deterministic) data structures, they always provide approximated answers but with reliable ways to estimate possible errors. Fortunately, the potential losses and errors are fully compensated for by extremely low memory requirements, constant query time, and scaling, the three factors that become essential in Big Data applications.About the bookThe purpose of this book is to introduce technology practitioners which includes software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms.While it is impossible to cover all the existing amazing solutions, this book is to highlight their common ideas and important areas of application, including membership querying, counting, stream mining, and similarity estimation.This is not a book for scientists only, but to gain the most out of it you will need to have basic mathematical knowledge and an understanding of the general theory of data structures and algorithms.What you will learnReading the book, you will get a theoretical and practical understanding of probabilistic data structures and learn about their common uses.
Hashing Membership Cardinality Frequency Rank SimilarityThis book on the WebYou can find errata, examples, and additional information at pdsa.gakhov.com. If you have a comment, technical question about the book, would like to report an error you found, or any other issue, send email to [email protected] case you are also interested in Cython implementation that includes many of the data structures and algorithms from this book, please check out our free and open-source Python library called PDSA at https://github.com/gakhov/pdsa. Everybody is welcome to contribute at any time.
- Learn how to solve practical issues of massive data handling
- Master the theoretical aspects of probabilistic data structures
- Identify the right data structures for your particular problems
Published by: gakhov | Publication date: 02/18/2019Kindle book details: Kindle Edition, 158 pages
Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. 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 for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The book includes solutions to all quizzes and selected problems, and a series of YouTube videos by the author accompanies the book. Part 2 of this book series covers covers graph search and its applications, shortest-path algorithms, and the applications and implementation of several data structures: heaps, search trees, hash tables, and bloom filters.
Published by: Soundlikeyourself Publishing, LLC | Publication date: 08/04/2018Kindle book details: Kindle Edition, 158 pages
With its focus on creating efficient data structures and algorithms, this comprehensive text helps readers understand how to select or design the tools that will best solve specific problems. It uses Microsoft C++ as the programming language and is suitable for second-year data structure courses and computer science courses in algorithm analysis.Techniques for representing data are presented within the context of assessing costs and benefits, promoting an understanding of the principles of algorithm analysis and the effects of a chosen physical medium. The text also explores tradeoff issues, familiarizes readers with the most commonly used data structures and their algorithms, and discusses matching appropriate data structures to applications. The author offers explicit coverage of design patterns encountered in the course of programming the book's basic data structures and algorithms. Numerous examples appear throughout the text.
Published by: Dover Publications | Publication date: 09/14/2011Kindle book details: Kindle Edition, 624 pages
Data Structures: Abstraction and Design Using Java, 3rd Edition, combines a strong emphasis on problem solving and software design with the study of data structures. The authors discuss applications of each data structure to motivate its study. After providing the specification (interface) and the implementation (a Java class), case studies that use the data structure to solve a significant problem are introduced.
Published by: Wiley | Publication date: 12/29/2015Kindle book details: Kindle Edition, 624 pages