R for Data Science, Grolemund Garrett, Wickham Hadley
Новое издание
Автор: Wickham, Hadley Cetinkaya-rundel, Mine Grolemund, Garrett Название: R for data science, 2 ed. ISBN: 1492097403 ISBN-13(EAN): 9781492097402 Издательство: Wiley Цена: 10136.00 р. Наличие на складе: Есть у поставщикаПоставка под заказ.
Описание: In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data.
Описание: This revised and updated edition of the bestseller provides a complete library of dimensional modeling techniques, the most comprehensive collection ever written.
Автор: Foster Provost Название: Data Science For Business: What You Need To Know About Data Mining And Dataanalytic Thinking ISBN: 1449361323 ISBN-13(EAN): 9781449361327 Издательство: Wiley Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть (1 шт.) Описание: This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
Автор: Guangren Shi Название: Data Mining and Knowledge Discovery for Geoscientists ISBN: 0124104371 ISBN-13(EAN): 9780124104372 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Поставка под заказ.
Описание: Addresses challenges by summarizing the developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information. This title focuses on 22 of data mining`s practical algorithms and application samples.
Автор: Ray Joyce M Название: Research Data Management ISBN: 1557536643 ISBN-13(EAN): 9781557536648 Издательство: Turpin Рейтинг: Цена: 4796.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximise the return on investment of public funds.To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management, and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers’ ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations. Contributors include: James L. Mullins, Purdue University; MacKenzie Smith, University of California at Davis; Sherry Lake, University of Virginia; Bernard Reilly, Center for Research Libraries; Jacob Carlson, Purdue University; Melissa Levine, University of Michigan; Jenn Riley, University of North Carolina at Chapel Hill; Jan Brase, German National Library of Science and Technology; Seamus Ross, University of Toronto; Michele Kimpton, DuraSpace; Brian Schottlaender, University of California, San Diego; Suzie Allard, University of Tennessee; Angus Whyte, Digital Curation Centre; Scott Brandt, Purdue University; Brian Westra, University of Oregon; Geneva Henry, Rice University; Gail Steinhart, Cornell University; and Cliff Lynch, Coalition for Networked Information.
Learn how to take full advantage of Apache Kafka, the distributed, publish-subscribe queue for handling real-time data feeds. With this comprehensive book, you ll understand how Kafka works and how it s designed. Authors Neha Narkhede, Gwen Shapira, and Todd Palino show you how to deploy production Kafka clusters; secure, tune, and monitor them; write rock-solid applications that use Kafka; and build scalable stream-processing applications.Learn how Kafka compares to other queues, and where it fits in the big data ecosystemDive into Kafka s internal designPick up best practices for developing applications that use KafkaUnderstand the best way to deploy Kafka in production monitoring, tuning, and maintenance tasksLearn how to secure a Kafka clusterGet detailed use-cases"
Автор: Hsu, Hui-Huang Название: Big Data Analytics for Sensor-Network Collected Intelligence ISBN: 0128093935 ISBN-13(EAN): 9780128093931 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
Contains contributions from noted scholars in computer science and electrical engineering from around the globe
Provides a broad overview of recent developments in sensor collected intelligence
Edited by a team comprised of leading thinkers in big data analytics
Автор: Steele Название: Algorithms for Data Science ISBN: 3319457950 ISBN-13(EAN): 9783319457956 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:
(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.
(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.
(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
Автор: Willem Mertens and Amedeo Pugliese Название: Quantitative Data Analysis ISBN: 3319426990 ISBN-13(EAN): 9783319426990 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in.
Автор: David Nettleton Название: Commercial Data Mining ISBN: 0124166024 ISBN-13(EAN): 9780124166028 Издательство: Elsevier Science Рейтинг: Цена: 6230.00 р. Наличие на складе: Поставка под заказ.
Описание: Helps you learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. This book guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.
Автор: С.Aggarwal Название: Data Mining: The Textbook ISBN: 3319141414 ISBN-13(EAN): 9783319141411 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Поставка под заказ.
Описание: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
Автор: Munzner Название: Visualization Analysis and Design ISBN: 1466508914 ISBN-13(EAN): 9781466508910 Издательство: Taylor&Francis Рейтинг: Цена: 10717.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Learn How to Design Effective Visualization Systems
Visualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques for spatial data, and visual analytics techniques for interweaving data transformation and analysis with interactive visual exploration. It emphasizes the careful validation of effectiveness and the consideration of function before form.
The book breaks down visualization design according to three questions: what data users need to see, why users need to carry out their tasks, and how the visual representations proposed can be constructed and manipulated. It walks readers through the use of space and color to visually encode data in a view, the trade-offs between changing a single view and using multiple linked views, and the ways to reduce the amount of data shown in each view. The book concludes with six case studies analyzed in detail with the full framework.
The book is suitable for a broad set of readers, from beginners to more experienced visualization designers. It does not assume any previous experience in programming, mathematics, human-computer interaction, or graphic design and can be used in an introductory visualization course at the graduate or undergraduate level.
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