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XML and Web Technologies for Data Sciences with R

XML and Web Technologies for Data Sciences with R Author Deborah Nolan
ISBN-10 9781461479000
Release 2013-11-29
Pages 663
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Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps. In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications. This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists. It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web. Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via Google Docs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data. These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies. The book contains many examples and case-studies that readers can use directly and adapt to their own work. The authors have focused on the integration of these technologies with the R statistical computing environment. However, the ideas and skills presented here are more general, and statisticians who use other computing environments will also find them relevant to their work. Deborah Nolan is Professor of Statistics at University of California, Berkeley. Duncan Temple Lang is Associate Professor of Statistics at University of California, Davis and has been a member of both the S and R development teams.



XML and Web Technologies for Data Sciences with R

XML and Web Technologies for Data Sciences with R Author Deborah Nolan
ISBN-10 1461478995
Release 2013-11-29
Pages 663
Download Link Click Here

Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps. In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications. This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists. It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web. Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via Google Docs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data. These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies. The book contains many examples and case-studies that readers can use directly and adapt to their own work. The authors have focused on the integration of these technologies with the R statistical computing environment. However, the ideas and skills presented here are more general, and statisticians who use other computing environments will also find them relevant to their work. Deborah Nolan is Professor of Statistics at University of California, Berkeley. Duncan Temple Lang is Associate Professor of Statistics at University of California, Davis and has been a member of both the S and R development teams.



Automated Data Collection with R

Automated Data Collection with R Author Simon Munzert
ISBN-10 9781118834817
Release 2015-01-20
Pages 480
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"This book provides a unified framework of web scraping and information extraction from text data with R for the social sciences"--



Data Science in R

Data Science in R Author Deborah Nolan
ISBN-10 9781482234824
Release 2015-04-21
Pages 539
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Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers’ computational reasoning of real-world data analyses.



Deborah Nolan Duncan Temple Lang XML and Web Technologies for Data Sciences with R

Deborah Nolan  Duncan Temple Lang  XML and Web Technologies for Data Sciences with R Author Simon Munzert
ISBN-10 OCLC:950706678
Release 2015
Pages
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Deborah Nolan Duncan Temple Lang XML and Web Technologies for Data Sciences with R has been writing in one form or another for most of life. You can find so many inspiration from Deborah Nolan Duncan Temple Lang XML and Web Technologies for Data Sciences with R also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Deborah Nolan Duncan Temple Lang XML and Web Technologies for Data Sciences with R book for free.



Data Science at the Command Line

Data Science at the Command Line Author Jeroen Janssens
ISBN-10 9781491947807
Release 2014-09-25
Pages 212
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This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms



Introduction to Data Technologies

Introduction to Data Technologies Author Paul Murrell
ISBN-10 1420065181
Release 2009-02-23
Pages 418
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Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve their awareness of what tasks can be achieved and describes the correct approach to perform these tasks. Practical examples demonstrate the most important points The author first discusses how to write computer code using HTML as a concrete example. He then covers a variety of data storage topics, including different file formats, XML, and the structure and design issues of relational databases. After illustrating how to extract data from a relational database using SQL, the book presents tools and techniques for searching, sorting, tabulating, and manipulating data. It also introduces some very basic programming concepts as well as the R language for statistical computing. Each of these topics has supporting chapters that offer reference material on HTML, CSS, XML, DTD, SQL, R, and regular expressions. One-stop shop of introductory computing information Written by a member of the R Development Core Team, this resource shows readers how to apply data technologies to tasks within a research setting. Collecting material otherwise scattered across many books and the web, it explores how to publish information via the web, how to access information stored in different formats, and how to write small programs to automate simple, repetitive tasks.



Voice Enabling Web Applications

Voice Enabling Web Applications Author Ken Abbott
ISBN-10 9781430208501
Release 2008-01-01
Pages 250
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Developers can use the same technology to build both automated voice services as well as visual Web sites, and Ken Abbott shows how in this comprehensive introduction to the syntax, concepts and strategies relevant to VoiceXML.



Web Technologies

Web Technologies Author Jeffrey C. Jackson
ISBN-10 9780133001976
Release 2011-11-21
Pages 592
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This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Web Technologies: A Computer Science Perspective is ideal for courses in Web-based Systems (aka Web/Internet Programming/Systems) in Computer Science, MIS, and IT departments. This text introduces the key technologies that have been developed as part of the birth and maturation of the World Wide Web. It provides a consistent, in-depth treatment of technologies that are unlikely to receive detailed coverage in non-Web computer science courses. Students will find an ongoing case study that integrates a wide spectrum of Web technologies, guidance on setting up their own software environments, and a variety of exercises and project assignments.



Web Data Management

Web Data Management Author Serge Abiteboul
ISBN-10 9781139505055
Release 2011-11-28
Pages
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The Internet and World Wide Web have revolutionized access to information. Users now store information across multiple platforms from personal computers to smartphones and websites. As a consequence, data management concepts, methods and techniques are increasingly focused on distribution concerns. Now that information largely resides in the network, so do the tools that process this information. This book explains the foundations of XML with a focus on data distribution. It covers the many facets of distributed data management on the Web, such as description logics, that are already emerging in today's data integration applications and herald tomorrow's semantic Web. It also introduces the machinery used to manipulate the unprecedented amount of data collected on the Web. Several 'Putting into Practice' chapters describe detailed practical applications of the technologies and techniques. The book will serve as an introduction to the new, global, information systems for Web professionals and master's level courses.



Data Mining with Rattle and R

Data Mining with Rattle and R Author Graham Williams
ISBN-10 9781441998903
Release 2011-08-04
Pages 374
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Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.



Federal Data Science

Federal Data Science Author Feras A. Batarseh
ISBN-10 9780128124444
Release 2017-09-21
Pages 256
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Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making. No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective. Offers a range of data science models, engineering tools, and federal use-cases Provides foundational observations into government data resources and requirements Introduces experiences and examples of data openness from the US and other countries A step-by-step guide for the conversion of government towards data-driven policy making Focuses on presenting data models that work within the constraints of the US government Presents the why, the what, and the how of injecting AI into federal culture and software systems



R for Data Science

R for Data Science Author Hadley Wickham
ISBN-10 9781491910368
Release 2016-12-12
Pages 520
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results



Data Mashups in R

Data Mashups in R Author Jeremy Leipzig
ISBN-10 9781449307257
Release 2011-03-04
Pages 40
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How do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia. This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis. Parse messy data from public foreclosure auction postings Plot the data using R's PBSmapping package Import US Census data to add context to foreclosure data Use R's lattice and latticeExtra packages for data visualization Create multidimensional correlation graphs with the pairs() scatterplot matrix package



Intelligent Techniques for Data Science

Intelligent Techniques for Data Science Author Rajendra Akerkar
ISBN-10 9783319292069
Release 2016-10-11
Pages 272
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This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.



XML Topic Maps

XML Topic Maps Author Jack Park
ISBN-10 0201749602
Release 2003
Pages 605
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XML Topic Maps is designed to be a "living document" for managing information across the Web's interconnected resources. The book begins with a broad introduction and a tutorial on topic maps and XTM technology. The focus then shifts to strategies for creating and deploying the technology. Throughout, the latest theoretical perspectives are offered, alongside discussions of the challenges developers will face as the Web continues to evolve. Looking forward, the book's concluding chapters provide a road map to the future of topic map technology and the Semantic Web in general.



Web Services Essentials

Web Services Essentials Author Ethan Cerami
ISBN-10 9781449337179
Release 2002-02-14
Pages 308
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As a developer new to Web Services, how do you make sense of this emerging framework so you can start writing your own services today? This concise book gives programmers both a concrete introduction and a handy reference to XML web services, first by explaining the foundations of this new breed of distributed services, and then by demonstrating quick ways to create services with open-source Java tools.Web Services make it possible for diverse applications to discover each other and exchange data seamlessly via the Internet. For instance, programs written in Java and running on Solaris can find and call code written in C# that run on Windows XP, or programs written in Perl that run on Linux, without any concern about the details of how that service is implemented. A common set of Web Services is at the core of Microsoft's new .NET strategy, Sun Microsystems's Sun One Platform, and the W3C's XML Protocol Activity Group.In this book, author Ethan Cerami explores four key emerging technologies: XML Remote Procedure Calls (XML-RPC) SOAP - The foundation for most commercial Web Services development Universal Discovery, Description and Integration (UDDI) Web Services Description Language (WSDL) For each of these topics, Web Services Essentials provides a quick overview, Java tutorials with sample code, samples of the XML documents underlying the service, and explanations of freely-available Java APIs. Cerami also includes a guide to the current state of Web Services, pointers to open-source tools and a comprehensive glossary of terms.If you want to break through the Web Services hype and find useful information on these evolving technologies, look no further than Web Services Essentials.