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Añadir al carritopaperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Añadir al carritoPaperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 2.
Librería: Toscana Books, AUSTIN, TX, Estados Unidos de America
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Añadir al carritoPaperback. Condición: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: California Books, Miami, FL, Estados Unidos de America
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Publicado por Packt Publishing 2016-07, 2016
ISBN 10: 1786466457 ISBN 13: 9781786466457
Idioma: Inglés
Librería: Chiron Media, Wallingford, Reino Unido
EUR 55,20
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: Russell Books, Victoria, BC, Canada
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Añadir al carritoSoftcover. Condición: New. Special order direct from the distributor.
Publicado por Packt Publishing Limited, 2016
ISBN 10: 1786466457 ISBN 13: 9781786466457
Idioma: Inglés
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
EUR 68,35
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por Packt Publishing Limited, 2016
ISBN 10: 1786466457 ISBN 13: 9781786466457
Idioma: Inglés
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 61,71
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Añadir al carritoPAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 87,03
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Utilize R to uncover hidden patterns in your Big DataKey FeaturesPerform computational analyses on Big Data to generate meaningful resultsGet a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databasesExplore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketBook DescriptionBig Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing.The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.What you will learnLearn about current state of Big Data processing using R programming language and its powerful statistical capabilitiesDeploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving mannerApply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usageExplore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platformWho this book is forThis book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows.It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.