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| common sense guide to data structures and algorithms: A Common-Sense Guide to Data Structures and Algorithms, Second Edition Jay Wengrow, 2020-08-10 Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable. |
| common sense guide to data structures and algorithms: A Common-Sense Guide to Data Structures and Algorithms Jay Wengrow, 2017-08-03 Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. This book takes a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. Graphics and examples make these computer science concepts understandable and relevant. You can use these techniques with any language; examples in the book are in JavaScript, Python, and Ruby. Use Big O notation, the primary tool for evaluating algorithms, to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Jay Wengrow brings to this book the key teaching practices he developed as a web development bootcamp founder and educator. Use these techniques today to make your code faster and more scalable. |
| common sense guide to data structures and algorithms: A Common-Sense Guide to Data Structures and Algorithms in Javascript, Volume 1 Jay Wengrow, 2024-09-24 If you thought data structures and algorithms were all just theory, you're missing out on what they can do for your JavaScript 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. Every chapter features practice exercises to give you the hands-on information you need to master data structures and algorithms for your day-to-day work. Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. The JavaScript edition uses JavaScript exclusively for all code examples, exercises, and solutions. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your JavaScript code faster and more scalable. What You Need: Certain code examples take advantage of recently introduced JavaScript features. Therefore, it's important to use a JavaScript environment that supports ECMAScript 6+ or a newer version. |
| common sense guide to data structures and algorithms: JavaScript Data Structures and Algorithms Sammie Bae, 2019-01-23 Explore data structures and algorithm concepts and their relation to everyday JavaScript development. A basic understanding of these ideas is essential to any JavaScript developer wishing to analyze and build great software solutions. You'll discover how to implement data structures such as hash tables, linked lists, stacks, queues, trees, and graphs. You'll also learn how a URL shortener, such as bit.ly, is developed and what is happening to the data as a PDF is uploaded to a webpage. This book covers the practical applications of data structures and algorithms to encryption, searching, sorting, and pattern matching. It is crucial for JavaScript developers to understand how data structures work and how to design algorithms. This book and the accompanying code provide that essential foundation for doing so. With JavaScript Data Structures and Algorithms you can start developing your knowledge and applying it to your JavaScript projects today. What You'll Learn Review core data structure fundamentals: arrays, linked-lists, trees, heaps, graphs, and hash-tableReview core algorithm fundamentals: search, sort, recursion, breadth/depth first search, dynamic programming, bitwise operators Examine how the core data structure and algorithms knowledge fits into context of JavaScript explained using prototypical inheritance and native JavaScript objects/data types Take a high-level look at commonly used design patterns in JavaScript Who This Book Is For Existing web developers and software engineers seeking to develop or revisit their fundamental data structures knowledge; beginners and students studying JavaScript independently or via a course or coding bootcamp. |
| common sense guide to data structures and algorithms: The Pragmatic Programmer Andrew Hunt, David Thomas, 1999-10-20 What others in the trenches say about The Pragmatic Programmer... “The cool thing about this book is that it’s great for keeping the programming process fresh. The book helps you to continue to grow and clearly comes from people who have been there.” — Kent Beck, author of Extreme Programming Explained: Embrace Change “I found this book to be a great mix of solid advice and wonderful analogies!” — Martin Fowler, author of Refactoring and UML Distilled “I would buy a copy, read it twice, then tell all my colleagues to run out and grab a copy. This is a book I would never loan because I would worry about it being lost.” — Kevin Ruland, Management Science, MSG-Logistics “The wisdom and practical experience of the authors is obvious. The topics presented are relevant and useful.... By far its greatest strength for me has been the outstanding analogies—tracer bullets, broken windows, and the fabulous helicopter-based explanation of the need for orthogonality, especially in a crisis situation. I have little doubt that this book will eventually become an excellent source of useful information for journeymen programmers and expert mentors alike.” — John Lakos, author of Large-Scale C++ Software Design “This is the sort of book I will buy a dozen copies of when it comes out so I can give it to my clients.” — Eric Vought, Software Engineer “Most modern books on software development fail to cover the basics of what makes a great software developer, instead spending their time on syntax or technology where in reality the greatest leverage possible for any software team is in having talented developers who really know their craft well. An excellent book.” — Pete McBreen, Independent Consultant “Since reading this book, I have implemented many of the practical suggestions and tips it contains. Across the board, they have saved my company time and money while helping me get my job done quicker! This should be a desktop reference for everyone who works with code for a living.” — Jared Richardson, Senior Software Developer, iRenaissance, Inc. “I would like to see this issued to every new employee at my company....” — Chris Cleeland, Senior Software Engineer, Object Computing, Inc. “If I’m putting together a project, it’s the authors of this book that I want. . . . And failing that I’d settle for people who’ve read their book.” — Ward Cunningham Straight from the programming trenches, The Pragmatic Programmer cuts through the increasing specialization and technicalities of modern software development to examine the core process--taking a requirement and producing working, maintainable code that delights its users. It covers topics ranging from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you'll learn how to Fight software rot; Avoid the trap of duplicating knowledge; Write flexible, dynamic, and adaptable code; Avoid programming by coincidence; Bullet-proof your code with contracts, assertions, and exceptions; Capture real requirements; Test ruthlessly and effectively; Delight your users; Build teams of pragmatic programmers; and Make your developments more precise with automation. Written as a series of self-contained sections and filled with entertaining anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you'll quickly see improvements in personal productivity, accuracy, and job satisfaction. You'll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You'll become a Pragmatic Programmer. |
| common sense guide to data structures and algorithms: Computer Science Distilled Wladston Ferreira Filho, 2017-01-17 A walkthrough of computer science concepts you must know. Designed for readers who don't care for academic formalities, it's a fast and easy computer science guide. It teaches the foundations you need to program computers effectively. After a simple introduction to discrete math, it presents common algorithms and data structures. It also outlines the principles that make computers and programming languages work. |
| common sense guide to data structures and algorithms: Advanced Algorithms and Data Structures Marcello La Rocca, 2021-06-29 This book introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Youll discover cutting-edge approaches to a variety of tricky scenarios. -- |
| common sense guide to data structures and algorithms: The Self-Taught Computer Scientist Cory Althoff, 2021-09-16 The follow-up to Cory Althoff's bestselling The Self-Taught Programmer, which inspired hundreds of thousands of professionals to learn to program outside of school! Fresh out of college and with just a year of self-study behind him, Cory Althoff was offered a dream first job as a software engineer for a well-known tech company, but he quickly found himself overwhelmed by the amount of things he needed to know, but hadn’t learned yet. This experience combined with his personal journey learning to program inspired his widely praised guide, The Self-Taught Programmer. Now Cory's back with another guide for the self-taught community of learners focusing on the foundations of computer science. The Self-Taught Computer Scientist introduces beginner and self-taught programmers to computer science fundamentals that are essential for success in programming and software engineering fields. Computer science is a massive subject that could cover an entire lifetime of learning. This book does not aim to cover everything you would learn about if you went to school to get a computer science degree. Instead, Cory's goal is to give you an introduction to some of the most important concepts in computer science that apply to a programming career. With a focus on data structures and algorithms, The Self-Taught Computer Scientist helps you fill gaps in your knowledge, prepare for a technical interview, feel knowledgeable and confident on the job, and ultimately, become a better programmer. Learn different algorithms including linear and binary search and test your knowledge with feedback loops Understand what a data structure is and study arrays, linked lists, stacks, queues, hash tables, binary trees, binary heaps, and graphs Prepare for technical interviews and feel comfortable working with more experienced colleagues Discover additional resources and tools to expand your skillset and continue your learning journey It's as simple as this: You have to study computer science if you want to become a successful programmer, and if you don't understand computer science, you won't get hired. Ready for a career in programming, coding, or software engineering and willing to embrace an always be learning mindset? The Self-Taught Computer Scientist is for you. |
| common sense guide to data structures and algorithms: A Practical Introduction to Data Structures and Algorithm Analysis Clifford A. Shaffer, 2001 This practical text contains fairly traditional coverage of data structures with a clear and complete use of algorithm analysis, and some emphasis on file processing techniques as relevant to modern programmers. It fully integrates OO programming with these topics, as part of the detailed presentation of OO programming itself.Chapter topics include lists, stacks, and queues; binary and general trees; graphs; file processing and external sorting; searching; indexing; and limits to computation.For programmers who need a good reference on data structures. |
| common sense guide to data structures and algorithms: Introduction to Algorithms, third edition Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, 2009-07-31 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. |
| common sense guide to data structures and algorithms: The Algorithm Design Manual Steven S Skiena, 2009-04-05 This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW war stories relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java |
| common sense guide to data structures and algorithms: The Bible of Algorithms and Data Structures Florian Dedov, 2020-08-22 The Most Important Skill in Computer Science! The field of algorithms and data structures is one of the most important in computer science. You will rarely be invited to a coding interview at Google, Microsoft or Facebook and not be asked questions about it. This is because these companies know how valuable the skills taught are. It doesn't matter if you are into machine learning, ethical hacking, cyber security or enterprise software engineering. You will always need to be able to work with algorithms and data structures. However, this field is also by many considered to be one of the hardest, since it is so abstract and complex. This is mainly due to the style in which it is taught. Most professors in colleges focus on exact mathematical definitions instead of understanding. And while you can't blame them for doing their job, there are better ways to learn about this subject. This book is for everyone who is interested in an intuitive and simple approach to algorithms and data structures. It is for everyone who is frustrated with memorizing dry formal definitions. This bible covers all the formal definitions that are important and necessary but it mainly focuses on breaking complex things down in a simple way. At the end, you will not only know how to formally analyze algorithms but you will also deeply understand what is happening behind the scenes and why things are the way they are. After Reading This Book You Will Have The Following Skills: - Intuitive understanding of algorithms and data structures - Analyzing the runtime complexity of algorithms - Using the Big O notation - Dissecting and analyzing sorting algorithms (Bubble Sort, Merge Sort, Quick Sort...) - Understanding and applying graph theory and related algorithms (BFS, DFS, Kruskal, Dijkstra) - Understanding basic data structures and their time complexities (Linked Lists, Stacks, Heaps, Trees...) - Using self-balancing trees (AVL, B-Tree...) - Understanding and applying hashing and collision resolution Master Algorithms and Data Structure Simply and Intuitively! |
| common sense guide to data structures and algorithms: Introduction to Algorithms, fourth edition Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, 2022-04-05 A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learning New material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays 140 new exercises and 22 new problems Reader feedback–informed improvements to old problems Clearer, more personal, and gender-neutral writing style Color added to improve visual presentation Notes, bibliography, and index updated to reflect developments in the field Website with new supplementary material Warning: Avoid counterfeit copies of Introduction to Algorithms by buying only from reputable retailers. Counterfeit and pirated copies are incomplete and contain errors. |
| common sense guide to data structures and algorithms: Java 9 Data Structures and Algorithms Debasish Ray Chawdhuri, 2017-04-28 Gain a deep understanding of the complexity of data structures and algorithms and discover the right way to write more efficient code About This Book This book provides complete coverage of reactive and functional data structures Based on the latest version of Java 9, this book illustrates the impact of new features on data structures Gain exposure to important concepts such as Big-O Notation and Dynamic Programming Who This Book Is For This book is for Java developers who want to learn about data structures and algorithms. Basic knowledge of Java is assumed. What You Will Learn Understand the fundamentals of algorithms, data structures, and measurement of complexity Find out what general purpose data structures are, including arrays, linked lists, double ended linked lists, and circular lists Get a grasp on the basics of abstract data types—stack, queue, and double ended queue See how to use recursive functions and immutability while understanding and in terms of recursion Handle reactive programming and its related data structures Use binary search, sorting, and efficient sorting—quicksort and merge sort Work with the important concept of trees and list all nodes of the tree, traversal of tree, search trees, and balanced search trees Apply advanced general purpose data structures, priority queue-based sorting, and random access immutable linked lists Gain a better understanding of the concept of graphs, directed and undirected graphs, undirected trees, and much more In Detail Java 9 Data Structures and Algorithms covers classical, functional, and reactive data structures, giving you the ability to understand computational complexity, solve problems, and write efficient code. This book is based on the Zero Bug Bounce milestone of Java 9. We start off with the basics of algorithms and data structures, helping you understand the fundamentals and measure complexity. From here, we introduce you to concepts such as arrays, linked lists, as well as abstract data types such as stacks and queues. Next, we'll take you through the basics of functional programming while making sure you get used to thinking recursively. We provide plenty of examples along the way to help you understand each concept. You will get the also get a clear picture of reactive programming, binary searches, sorting, search trees, undirected graphs, and a whole lot more! Style and approach This book will teach you about all the major algorithms in a step-by-step manner. Special notes on the Big-O Notation and its impact on algorithms will give you fresh insights. |
| common sense guide to data structures and algorithms: Data Structures and Algorithms in Java Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, 2014-01-28 The design and analysis of efficient data structures has long been recognized as a key component of the Computer Science curriculum. Goodrich, Tomassia and Goldwasser's approach to this classic topic is based on the object-oriented paradigm as the framework of choice for the design of data structures. For each ADT presented in the text, the authors provide an associated Java interface. Concrete data structures realizing the ADTs are provided as Java classes implementing the interfaces. The Java code implementing fundamental data structures in this book is organized in a single Java package, net.datastructures. This package forms a coherent library of data structures and algorithms in Java specifically designed for educational purposes in a way that is complimentary with the Java Collections Framework. |
| common sense guide to data structures and algorithms: Problem Solving with Algorithms and Data Structures Using Python Bradley N. Miller, David L. Ranum, 2011 Thes book has three key features : fundamental data structures and algorithms; algorithm analysis in terms of Big-O running time in introducied early and applied throught; pytohn is used to facilitates the success in using and mastering data strucutes and algorithms. |
| common sense guide to data structures and algorithms: Dive Into Algorithms Bradford Tuckfield, 2021-01-25 Dive Into Algorithms is a broad introduction to algorithms using the Python Programming Language. Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares. You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and another for generating seemingly random sets of numbers. You'll also learn how to: Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees Measure the efficiency and speed of algorithms Generate Voronoi diagrams for use in various geometric applications Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions Use simulated annealing to perform global optimization Build a decision tree to predict happiness based on a person's characteristics Once you've finished this book you'll understand how to code and implement important algorithms as well as how to measure and optimize their performance, all while learning the nitty-gritty details of today's most powerful algorithms. |
| common sense guide to data structures and algorithms: Algorithms and Data Structures for Massive Datasets Dzejla Medjedovic, Emin Tahirovic, 2022-08-16 Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting |
| common sense guide to data structures and algorithms: Essential Algorithms Rod Stephens, 2019-05-29 A friendly introduction to the most useful algorithms written in simple, intuitive English The revised and updated second edition of Essential Algorithms, offers an accessible introduction to computer algorithms. The book contains a description of important classical algorithms and explains when each is appropriate. The author shows how to analyze algorithms in order to understand their behavior and teaches techniques that the can be used to create new algorithms to meet future needs. The text includes useful algorithms such as: methods for manipulating common data structures, advanced data structures, network algorithms, and numerical algorithms. It also offers a variety of general problem-solving techniques. In addition to describing algorithms and approaches, the author offers details on how to analyze the performance of algorithms. The book is filled with exercises that can be used to explore ways to modify the algorithms in order to apply them to new situations. This updated edition of Essential Algorithms: Contains explanations of algorithms in simple terms, rather than complicated math Steps through powerful algorithms that can be used to solve difficult programming problems Helps prepare for programming job interviews that typically include algorithmic questions Offers methods can be applied to any programming language Includes exercises and solutions useful to both professionals and students Provides code examples updated and written in Python and C# Essential Algorithms has been updated and revised and offers professionals and students a hands-on guide to analyzing algorithms as well as the techniques and applications. The book also includes a collection of questions that may appear in a job interview. The book’s website will include reference implementations in Python and C# (which can be easily applied to Java and C++). |
| common sense guide to data structures and algorithms: Data Structures & Algorithms in Python Robert Lafore, Alan Broder, John Canning, 2022-09-06 LEARN HOW TO USE DATA STRUCTURES IN WRITING HIGH PERFORMANCE PYTHON PROGRAMS AND ALGORITHMS This practical introduction to data structures and algorithms can help every programmer who wants to write more efficient software. Building on Robert Lafore's legendary Java-based guide, this book helps you understand exactly how data structures and algorithms operate. You'll learn how to efficiently apply them with the enormously popular Python language and scale your code to handle today's big data challenges. Throughout, the authors focus on real-world examples, communicate key ideas with intuitive, interactive visualizations, and limit complexity and math to what you need to improve performance. Step-by-step, they introduce arrays, sorting, stacks, queues, linked lists, recursion, binary trees, 2-3-4 trees, hash tables, spatial data structures, graphs, and more. Their code examples and illustrations are so clear, you can understand them even if you're a near-beginner, or your experience is with other procedural or object-oriented languages. Build core computer science skills that take you beyond merely “writing code” Learn how data structures make programs (and programmers) more efficient See how data organization and algorithms affect how much you can do with today's, and tomorrow's, computing resources Develop data structure implementation skills you can use in any language Choose the best data structure(s) and algorithms for each programming problem—and recognize which ones to avoid Data Structures & Algorithms in Python is packed with examples, review questions, individual and team exercises, thought experiments, and longer programming projects. It's ideal for both self-study and classroom settings, and either as a primary text or as a complement to a more formal presentation. |
| common sense guide to data structures and algorithms: COMMON-SENSE GUIDE TO DATA STRUCTURES AND ALGORITHMS IN JAVASCRIPT, VOLUME 1 JAY. WENGROW, 2024 |
| common sense guide to data structures and algorithms: Data Structures and Algorithm Analysis in Java, Third Edition Clifford A. Shaffer, 2012-09-06 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses Java as the programming language. |
| common sense guide to data structures and algorithms: Data Structures and Algorithm Analysis in C++, Third Edition Clifford A. Shaffer, 2012-07-26 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses C++ as the programming language. |
| common sense guide to data structures and algorithms: Algorithms and Data Structures Kurt Mehlhorn, Peter Sanders, 2008-05-27 Algorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems. This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java. The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions. |
| common sense guide to data structures and algorithms: Data Structures and Algorithms with Python Kent D. Lee, Steve Hubbard, 2015-01-12 This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background. |
| common sense guide to data structures and algorithms: Data Structures and Algorithms in Python Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, 2013-06-17 Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects. |
| common sense guide to data structures and algorithms: Grokking Algorithms Aditya Bhargava, 2016-05-12 This book does the impossible: it makes math fun and easy! - Sander Rossel, COAS Software Systems Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel. Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/algorithms-?in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs. About the Book Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them. What's Inside Covers search, sort, and graph algorithms Over 400 pictures with detailed walkthroughs Performance trade-offs between algorithms Python-based code samples About the Reader This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. About the Author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents Introduction to algorithms Selection sort Recursion Quicksort Hash tables Breadth-first search Dijkstra's algorithm Greedy algorithms Dynamic programming K-nearest neighbors |
| common sense guide to data structures and algorithms: Statistics As Principled Argument Robert P. Abelson, 2012-09-10 In this illuminating volume, Robert P. Abelson delves into the too-often dismissed problems of interpreting quantitative data and then presenting them in the context of a coherent story about one's research. Unlike too many books on statistics, this is a remarkably engaging read, filled with fascinating real-life (and real-research) examples rather than with recipes for analysis. It will be of true interest and lasting value to beginning graduate students and seasoned researchers alike. The focus of the book is that the purpose of statistics is to organize a useful argument from quantitative evidence, using a form of principled rhetoric. Five criteria, described by the acronym MAGIC (magnitude, articulation, generality, interestingness, and credibility) are proposed as crucial features of a persuasive, principled argument. Particular statistical methods are discussed, with minimum use of formulas and heavy data sets. The ideas throughout the book revolve around elementary probability theory, t tests, and simple issues of research design. It is therefore assumed that the reader has already had some access to elementary statistics. Many examples are included to explain the connection of statistics to substantive claims about real phenomena. |
| common sense guide to data structures and algorithms: Exercises for Programmers Brian P. Hogan, 2015-09-04 When you write software, you need to be at the top of your game. Great programmers practice to keep their skills sharp. Get sharp and stay sharp with more than fifty practice exercises rooted in real-world scenarios. If you're a new programmer, these challenges will help you learn what you need to break into the field, and if you're a seasoned pro, you can use these exercises to learn that hot new language for your next gig. One of the best ways to learn a programming language is to use it to solve problems. That's what this book is all about. Instead of questions rooted in theory, this book presents problems you'll encounter in everyday software development. These problems are designed for people learning their first programming language, and they also provide a learning path for experienced developers to learn a new language quickly. Start with simple input and output programs. Do some currency conversion and figure out how many months it takes to pay off a credit card. Calculate blood alcohol content and determine if it's safe to drive. Replace words in files and filter records, and use web services to display the weather, store data, and show how many people are in space right now. At the end you'll tackle a few larger programs that will help you bring everything together. Each problem includes constraints and challenges to push you further, but it's up to you to come up with the solutions. And next year, when you want to learn a new programming language or style of programming (perhaps OOP vs. functional), you can work through this book again, using new approaches to solve familiar problems. What You Need: You need access to a computer, a programming language reference, and the programming language you want to use. |
| common sense guide to data structures and algorithms: Principles of Data Structures Using C and C++ Vinu V. Das, 2006 About the Book: Principles of DATA STRUCTURES using C and C++ covers all the fundamental topics to give a better understanding about the subject. The study of data structures is essential to every one who comes across with computer science. This book is written in accordance with the revised syllabus for B. Tech./B.E. (both Computer Science and Electronics branches) and MCA. students of Kerala University, MG University, Calicut University, CUSAT Cochin (deemed) University. NIT Calicut (deemed) University, Anna University, UP Technical University, Amritha Viswa (deemed) Vidyapeeth, Karunya (dee. |
| common sense guide to data structures and algorithms: The Secret Life of Programs Jonathan E. Steinhart, 2019-08-06 A primer on the underlying technologies that allow computer programs to work. Covers topics like computer hardware, combinatorial logic, sequential logic, computer architecture, computer anatomy, and Input/Output. Many coders are unfamiliar with the underlying technologies that make their programs run. But why should you care when your code appears to work? Because you want it to run well and not be riddled with hard-to-find bugs. You don't want to be in the news because your code had a security problem. Lots of technical detail is available online but it's not organized or collected into a convenient place. In The Secret Life of Programs, veteran engineer Jonathan E. Steinhart explores--in depth--the foundational concepts that underlie the machine. Subjects like computer hardware, how software behaves on hardware, as well as how people have solved problems using technology over time. You'll learn: How the real world is converted into a form that computers understand, like bits, logic, numbers, text, and colors The fundamental building blocks that make up a computer including logic gates, adders, decoders, registers, and memory Why designing programs to match computer hardware, especially memory, improves performance How programs are converted into machine language that computers understand How software building blocks are combined to create programs like web browsers Clever tricks for making programs more efficient, like loop invariance, strength reduction, and recursive subdivision The fundamentals of computer security and machine intelligence Project design, documentation, scheduling, portability, maintenance, and other practical programming realities. Learn what really happens when your code runs on the machine and you'll learn to craft better, more efficient code. |
| common sense guide to data structures and algorithms: Beginning C# Object-Oriented Programming Dan Clark, 2011-08-12 Beginning C# Object-Oriented Programming brings you into the modern world of development as you master the fundamentals of programming with C# and learn to develop efficient, reusable, elegant code through the object-oriented programming (OOP) methodology. Take your skills out of the 20th century and into this one with Dan Clark's accessible, quick-paced guide to C# and object-oriented programming, completely updated for .NET 4.0 and C# 4.0. As you develop techniques and best practices for coding in C#, one of the world's most popular contemporary languages, you'll experience modeling a “real world” application through a case study, allowing you to see how both C# and OOP (a methodology you can use with any number of languages) come together to make your code reusable, modern, and efficient. With more than 30 fully hands-on activities, you'll discover how to transform a simple model of an application into a fully-functional C# project, including designing the user interface, implementing the business logic, and integrating with a relational database for data storage. Along the way, you will explore the .NET Framework, the creation of a Windows-based user interface, a web-based user interface, and service-oriented programming, all using Microsoft's industry-leading Visual Studio 2010, C#, Silverlight, the Entity Framework, and more. |
| common sense guide to data structures and algorithms: Algorithms in a Nutshell George T. Heineman, Gary Pollice, Stanley Selkow, 2008-10-14 Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will: Solve a particular coding problem or improve on the performance of an existing solution Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips Learn the expected performance of an algorithm, and the conditions it needs to perform at its best Discover the impact that similar design decisions have on different algorithms Learn advanced data structures to improve the efficiency of algorithms With Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications. |
| common sense guide to data structures and algorithms: Beginning Java Data Structures and Algorithms James Cutajar, 2018-07-30 Though your application serves its purpose, it might not be a high performer. Learn techniques to accurately predict code efficiency, easily dismiss inefficient solutions, and improve the performance of your application. Key Features Explains in detail different algorithms and data structures with sample problems and Java implementations where appropriate Includes interesting tips and tricks that enable you to efficiently use algorithms and data structures Covers over 20 topics using 15 practical activities and exercises Book Description Learning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems. This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications. What you will learn Understand some of the fundamental concepts behind key algorithms Express space and time complexities using Big O notation. Correctly implement classic sorting algorithms such as merge and quicksort Correctly implement basic and complex data structures Learn about different algorithm design paradigms, such as greedy, divide and conquer, and dynamic programming Apply powerful string matching techniques and optimize your application logic Master graph representations and learn about different graph algorithms Who this book is for If you want to better understand common data structures and algorithms by following code examples in Java and improve your application efficiency, then this is the book for you. It helps to have basic knowledge of Java, mathematics and object-oriented programming techniques. |
| common sense guide to data structures and algorithms: Data Structures and Algorithms in C++ Michael T. Goodrich, Roberto Tamassia, David M. Mount, 2011-02-22 An updated, innovative approach to data structures and algorithms Written by an author team of experts in their fields, this authoritative guide demystifies even the most difficult mathematical concepts so that you can gain a clear understanding of data structures and algorithms in C++. The unparalleled author team incorporates the object-oriented design paradigm using C++ as the implementation language, while also providing intuition and analysis of fundamental algorithms. Offers a unique multimedia format for learning the fundamentals of data structures and algorithms Allows you to visualize key analytic concepts, learn about the most recent insights in the field, and do data structure design Provides clear approaches for developing programs Features a clear, easy-to-understand writing style that breaks down even the most difficult mathematical concepts Building on the success of the first edition, this new version offers you an innovative approach to fundamental data structures and algorithms. |
| common sense guide to data structures and algorithms: Distributed Services with Go Travis Jeffery, 2020-10-27 You know the basics of Go and are eager to put your knowledge to work. This book is just what you need to apply Go to real-world situations. You'll build a distributed service that's highly available, resilient, and scalable. Along the way you'll master the techniques, tools, and tricks that skilled Go programmers use every day to build quality applications. Level up your Go skills today. Take your Go skills to the next level by learning how to design, develop, and deploy a distributed service. Start from the bare essentials of storage handling, then work your way through networking a client and server, and finally to distributing server instances, deployment, and testing. All this will make coding in your day job or side projects easier, faster, and more fun. Lay out your applications and libraries to be modular and easy to maintain. Build networked, secure clients and servers with gRPC. Monitor your applications with metrics, logs, and traces to make them debuggable and reliable. Test and benchmark your applications to ensure they're correct and fast. Build your own distributed services with service discovery and consensus. Write CLIs to configure your applications. Deploy applications to the cloud with Kubernetes and manage them with your own Kubernetes Operator. Dive into writing Go and join the hundreds of thousands who are using it to build software for the real world. What You Need: Go 1.11 and Kubernetes 1.12. |
| common sense guide to data structures and algorithms: Data Structures and Algorithm Analysis in C+ Mark Allen Weiss, 2003 In this second edition of his successful book, experienced teacher and author Mark Allen Weiss continues to refine and enhance his innovative approach to algorithms and data structures. Written for the advanced data structures course, this text highlights theoretical topics such as abstract data types and the efficiency of algorithms, as well as performance and running time. Before covering algorithms and data structures, the author provides a brief introduction to C++ for programmers unfamiliar with the language. Dr Weiss's clear writing style, logical organization of topics, and extensive use of figures and examples to demonstrate the successive stages of an algorithm make this an accessible, valuable text. New to this Edition *An appendix on the Standard Template Library (STL) *C++ code, tested on multiple platforms, that conforms to the ANSI ISO final draft standard 0201361221B04062001 |
| common sense guide to data structures and algorithms: Data Structures And Algorithms Shi-kuo Chang, 2003-09-29 This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at www.cs.pitt.edu/~jung/GrowingBook/, so that both teachers and students can benefit from their expertise. |
| common sense guide to data structures and algorithms: A Common-Sense Guide to Data Structures and Algorithms in Python, Volume 1 Jay Wengrow, 2023 With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. Every chapter features practice exercises to give you the hands-on information you need to master data structures and algorithms for your day-to-day work. |
| common sense guide to data structures and algorithms: Data Structures Using C Reema Thareja, 2014 This second edition of Data Structures Using C has been developed to provide a comprehensive and consistent coverage of both the abstract concepts of data structures as well as the implementation of these concepts using C language. It begins with a thorough overview of the concepts of C programming followed by introduction of different data structures and methods to analyse the complexity of different algorithms. It then connects these concepts and applies them to the study of various data structures such as arrays, strings, linked lists, stacks, queues, trees, heaps, and graphs. The book utilizes a systematic approach wherein the design of each of the data structures is followed by algorithms of different operations that can be performed on them, and the analysis of these algorithms in terms of their running times. Each chapter includes a variety of end-chapter exercises in the form of MCQs with answers, review questions, and programming exercises to help readers test their knowledge. |
A Common-Sense Guide to Data Structures and Algorithms, Second Edition
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