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Data Structures And Algorithms Using Java
With an accessible writing style and manageable amount of content, Data Structures and Algorithms Using Java is the ideal text for your course. This outstanding text correlates to the recommended syllabus put forth by the Association of Computing Machinery standard curriculum guidelines. The author has produced a resource that is more readable and instructional than any other, without compromising the scope of the ACM CS103, Data Structures and Algorithms, course material. The text’s unique, student-friendly pedagogical approach and organizational structure will keep students engaged in the process of self-directed investigative discovery both inside and outside the classroom.The pedagogical features of the text, based on the author’s 30 years of teaching experience, include succinct code examples, a unique common template used as the organizational basis of each chapter, the use of pseudocode to present the major algorithms developed in the text, nearly 300 carefully designed figures, and a concise review of Java.
R 6,148.00
Data Structures and Algorithms with JavaScript
As an experienced JavaScript developer moving to server-side programming, you need to implement classic data structures and algorithms associated with conventional object-oriented languages like C# and Java. This practical guide shows you how to work hands-on with a variety of storage mechanisms - including linked lists, stacks, queues, and graphs - within the constraints of the JavaScript environment. Determine which data structures and algorithms are most appropriate for the problems you're trying to solve, and understand the tradeoffs when using them in a JavaScript program. An overview of the JavaScript features used throughout the book is also included.This book covers: Arrays and lists: the most common data structures Stacks and queues: more complex list-like data structures Linked lists: how they overcome the shortcomings of arrays Dictionaries: storing data as key-value pairs Hashing: good for quick insertion and retrieval Sets: useful for storing unique elements that appear only once Binary Trees: storing data in a hierarchical manner Graphs and graph algorithms: ideal for modeling networks Algorithms: including those that help you sort or search data Advanced algorithms: dynamic programming and greedy algorithms
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Hands-On Data Structures and Algorithms with Kotlin
Understand and solve complex computational problems and write efficient code with KotlinKey FeaturesLearn about important data structures such as lists, arrays, queues, and stacksDesign custom algorithms for real-life implementationsIdentify suitable tools for different scenarios and deliver immediate resultsBook DescriptionData structures and algorithms are more than just theoretical concepts. They help you become familiar with computational methods for solving problems and writing logical code. Equipped with this knowledge, you can write efficient programs that run faster and use less memory.Hands-On Data Structures and Algorithms with Kotlin book starts with the basics of algorithms and data structures, helping you get to grips with the fundamentals and measure complexity. You'll then move on to exploring the basics of functional programming while getting used to thinking recursively. Packed with plenty of examples along the way, this book will help you grasp each concept easily. In addition to this, you'll get a clear understanding of how the data structures in Kotlin's collection framework work internally.By the end of this book, you will be able to apply the theory of data structures and algorithms to work out real-world problems.What you will learnUnderstand the basic principles of algorithms and data structuresExplore general-purpose data structures with arrays and linked listsGet to grips with the basics of stacks, queues, and double-ended queuesUnderstand functional programming and related data structuresUse performant searching and efficient sortingUncover how Kotlin's collection framework functionsBecome adept at implementing different types of mapsWho this book is forIf you're a Kotlin developer who wants to learn the intricacies of implementing data structures and algorithms for scalable application development, this book is for you.
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Data Structures and Algorithms in Python
Based on the authors’ market leading data structures books in Java and C++, this textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. 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++.
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Learning JavaScript Data Structures and Algorithms -
Hone your skills by learning classic data structures and algorithms in JavaScriptAbout This Book• Understand common data structures and the associated algorithms, as well as the context in which they are used.• Master existing JavaScript data structures such as array, set and map and learn how to implement new ones such as stacks, linked lists, trees and graphs.• All concepts are explained in an easy way, followed by examples.Who This Book Is ForIf you are a student of Computer Science or are at the start of your technology career and want to explore JavaScript's optimum ability, this book is for you. You need a basic knowledge of JavaScript and programming logic to start having fun with algorithms.What You Will Learn• Declare, initialize, add, and remove items from arrays, stacks, and queues• Get the knack of using algorithms such as DFS (Depth-first Search) and BFS (Breadth-First Search) for the most complex data structures• Harness the power of creating linked lists, doubly linked lists, and circular linked lists• Store unique elements with hash tables, dictionaries, and sets• Use binary trees and binary search trees• Sort data structures using a range of algorithms such as bubble sort, insertion sort, and quick sortIn DetailThis book begins by covering basics of the JavaScript language and introducing ECMAScript 7, before gradually moving on to the current implementations of ECMAScript 6. You will gain an in-depth knowledge of how hash tables and set data structure functions, as well as how trees and hash maps can be used to search files in a HD or represent a database. This book is an accessible route deeper into JavaScript. Graphs being one of the most complex data structures you'll encounter, we'll also give you a better understanding of why and how graphs are largely used in GPS navigation systems in social networks.Toward the end of the book, you'll discover how all the theories presented by this book can be applied in real-world solutions while working on your own computer networks and Facebook searches.Style and approachThis book gets straight to the point, providing you with examples of how a data structure or algorithm can be used and giving you real-world applications of the algorithm in JavaScript. With real-world use cases associated with each data structure, the book explains which data structure should be used to achieve the desired results in the real world.
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Schaum's Outline of Data Structures with Java, 2ed
Tough Test Questions? Missed Lectures? Not Enough Time?Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives youPractice problems with full explanations that reinforce knowledgeCoverage of the most up-to-date developments in your course fieldIn-depth review of practices and applicationsFully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores!Schaum's Outlines-Problem Solved.
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Learning JavaScript Data Structures and Algorithms
If you are a JavaScript developer or someone who has basic knowledge of JavaScript, and want to explore its optimum ability, this fast-paced book is definitely for you. Programming logic is the only thing you need to know to start having fun with algorithms.
R 1,661.00
Data Science Essentials in Python
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data.Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
R 1,004.00
Python and R for the Modern Data Scientist
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you'll discover new ways to accomplish data science tasks and expand your skill set.Authors Boyan Angelov and Rick Scavetta explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. Not only will you learn how to use Python and R together in real-world settings, but you'll also broaden your knowledge and job opportunities by working as a bilingual data scientist.Learn Python and R from the perspective of your current languageUnderstand the strengths and weaknesses of each languageIdentify use cases where one language is better suited than the otherUnderstand the modern open source ecosystem available for both, including packages, frameworks, and workflowsLearn how to integrate R and Python in a single workflowFollow a real-world case study that demonstrates ways to use these languages together
R 1,505.00
ClojureScript - Up and Running
Learn how to build complete client-side applications with ClojureScript, the Clojure language variant that compiles to optimized JavaScript. This hands-on introduction shows you how ClojureScript not only has similarities to JavaScript - without the flaws - but also supports the full semantics of its parent language. You'll delve into ClojureScript's immutable data structures, lazy sequences, first-class functions, macros, and support for JavaScript libraries. No previous experience with Clojure or ClojureScript is necessary. If you're familiar with JavaScript, HTML, CSS, and the DOM, you'll quickly discover that ClojureScript has the same reach as JavaScript, but with more power.Start writing ClojureScript code with the Leiningen build system Learn how the ClojureScript compiler works to produce optimized JavaScript Use JavaScript functions and libraries directly from ClojureScript code Explore functions in Clojure's sequence library such as map, reduce, and filter Use macros to define new control structures or embed domain-specific languages Compile manually or script your own workflow with ClojureScript's compiler tools Integrate ClojureScript with Clojure on the JVM to build powerful client-server applications
R 692.00
Advanced Applications and Structures in XML Processing
Advanced Applications and Structures in XML Processing: Label Streams, Semantics Utilization and Data Query Technologies reflects the significant research results and latest findings of scholars worldwide, working to explore and expand the role of XML. This collection represents an understanding of XML processing technologies in connection with both advanced applications and the latest XML processing technologies that is of primary importance. It provides the opportunity to understand topics in detail and discover XML research at a comprehensive level.
R 7,031.19
Learning IPython for Interactive Computing and Data Visualization -
Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This Book• Learn the basics of Python in the Jupyter Notebook• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallelWho This Book Is ForThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will Learn• Install Anaconda and code in Python in the Jupyter Notebook• Load and explore datasets interactively• Perform complex data manipulations effectively with pandas• Create engaging data visualizations with matplotlib and seaborn• Simulate mathematical models with NumPy• Visualize and process images interactively in the Jupyter Notebook with scikit-image• Accelerate your code with Numba, Cython, and IPython.parallel• Extend the Notebook interface with HTML, JavaScript, and D3In DetailPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approachThis is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
R 1,531.00
Introduction to Machine Learning with Python
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
R 2,076.00
Streaming Architecture
More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you ll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm.Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases.Ideal for developers and non-technical people alike, this book describes: Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layerNew messaging technologies, including Apache Kafka and MapR Streams, with links to sample codeTechnology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache ApexHow stream-based architectures are helpful to support microservicesSpecific use cases such as fraud detection and geo-distributed data streamsTed Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community.He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @teddunning.Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @EllenFriedman."
R 865.00
Cases on Open-Linked Data and Semantic Web Applications
With the purpose of building upon standard web technologies, open linked data serves as a useful way to connect previously unrelated data and to publish structured data on the web. The application of these elements leads to the creation of data commons called semantic web. Cases on Open-Linked Data and Semantic Web Applications brings together new theories, research findings and case studies which cover the recent developments and approaches towards applied open linked data and semantic web in the context of information systems. By enhancing the understanding of open linked data in business, science and information technologies, this reference source aims to be useful for academics, researchers, and practitioners. With the purpose of building upon standard web technologies, open linked data serves as a useful way to connect previously unrelated data and to publish structured data on the web. The application of these elements leads to the creation of data commons called semantic web.
R 6,790.29
Learning ClojureScript
Master the art of agile single page web application development with ClojureScriptAbout This Book• Set up interactive development workflows for the browser or Node.js thanks to the ClojureScript ecosystem• Learn the basics of interactive single page web app development taking advantage of the functional nature of ClojureScript• Delve into advanced rich web application development concepts such as Om, along with core.async, using zippers and logic programming, and preparing code for production with testing or optimizing via the Google Closure CompilerWho This Book Is ForThis book is for web application developers who want to benefit from the power of ClojureScript to get an agile and highly productive development platform that targets mainly browser JavaScript.You are not required to be fluent in Clojure, but it will be easier for you if you have a basic understanding of browser and server-side JavaScript.What You Will Learn• Understand how the ClojureScript compiler operates• Set up interactive development workflows for ClojureScript• Grasp the basics of the ClojureScript language, including basic syntax, data structures, variable scoping, namespaces, and finally the powerful sequence abstraction• Delve into advanced concepts such as functional programming, macro writing, asynchronous programming, app routing, and real-time web• Develop simple one page web applications• Explore techniques to make your web apps aware of the external world through external or embedded database access or Oauth 2 integration• Learn more advanced ClojureScript concepts like in app routing, real-time web• Prepare your work for production, getting insights into optional type-checking, writing portable Clojure/ClojureScript code, and testingIn DetailClojure is an expressive language that makes it possible to easily tackle complex software development challenges. Its bias toward interactive development has made it a powerful tool, enabling high developer productivity.In this book, you will first learn how to construct an interactive development experience for ClojureScript.. You will be guided through ClojureScript language concepts, looking at the basics first, then being introduced to advanced concepts such as functional programming or macro writing. After that, we elaborate on the subject of single page web applications, showcasing how to build a simple one, then covering different possible enhancements. We move on to study more advanced ClojureScript concepts, where you will be shown how to address some complex algorithmic cases. Finally, you'll learn about optional type-checking for your programs, how you can write portable code, test it, and put the advanced compilation mode of the Google Closure Compiler to good use.Style and approachThis book is a comprehensive reference guide on ClojureScript development for the front end, and will gradually help you master interactive ClojureScript development workflows, through detailed step-by-step information illustrated with
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