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Library items with class number 006.312

Big data analytics with R / Simon Walkowiak

No review available on this record. Title : Big data analytics with R : lerage R programming to uncover hidden patterns in your Big Data Material Type: printed text Authors: Simon Walkowiak, Author Publisher: Birmingham : Packt Publishing Publication Date: 2016 Pagination: v, 490 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-7864-6645-7 General note: Includes index (p. 483-490) Languages : English (eng) Original Language : English (eng) Descriptors: Big data Data mining Data processing - Software Information visualization - Software Class number: 006.312 Abstract: Utilize R to uncover hidden patterns in your Big DataAbout This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketWho 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. What You Will Learn Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner Apply 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 usage Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platformIn DetailBig 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. Style and approachThis book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets. Contents note: The Era of Big Data; Introduction to R Programming Language and Statistical Environment; Unleashing the Power of R from Within; Hadoop and MapReduce Framework for R; R with Relational Database Management Systems (RDBMSs); R with Non-Relational (NoSQL) Databases; Faster than Hadoop - Spark with R; Machine Learning Methods for Big Data in R; The Future of R - Big, Fast, and Smart Data; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=19068

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Barcode	Call number	Media type	Location	Section	Status
1702-002377	006.312 Wal-Big 2016	General Collection	Library "Max van der Stoel"	English	Available

Data analytics with Hadoop / Benjamin Bengfort

No review available on this record. Title : Data analytics with Hadoop : an introduction for data scientists Material Type: printed text Authors: Benjamin Bengfort, Author Publisher: Beijing : O'Reilly Publication Date: 2016 Pagination: xvi, 268 p. Size: 24 cm ISBN (or other code): 978-1-491-91370-3 General note: Includes bibliographical references and index Languages : English (eng) Original Language : English (eng) Descriptors: Apache Hadoop Big data Data mining Electronic data processing - Distributed processing File organization (Computer science) Class number: 006.312 Abstract: "Data analytics with Hadoop "- Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib Contents note: The age of the data product; An operating system for big data; A framework for Python and Hadoop streaming; In-memory computing with Spark; Distributed analysis and patterns; Data mining and warehousing; Data ingestion; Analytics with higher-level APIs; Machine learning; Summary : doing distributed data science; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17989

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Barcode	Call number	Media type	Location	Section	Status
1702-002338	006.312 Ben-Dat 2016	General Collection	Library "Max van der Stoel"	English	Available
1702-002339	006.312 Ben-Dat 2016	General Collection	SEEU Library Skopje	English	Available

Introduction to data mining / Pang-Ning Tan

No review available on this record. Title : Introduction to data mining Material Type: printed text Authors: Pang-Ning Tan, Author ; V. Kumar, Author ; Michael Steinbach, Author ; Anuj Karpatne, Author Publisher: New York : Pearson Education Publication Date: 2019 Pagination: xix, 839 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-0-13-312890-1 General note: Includes bibliographic references (p. 806-808) Includes indexes (p. 816-838) Languages : English (eng) Original Language : English (eng) Descriptors: Data mining Database management Knowledge acquisition (Expert systems) Class number: 006.312 Abstract: Introduction to Data Mining - gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth. Contents note: Introduction; Data; Classification : basic concepts and techniques; Classification : alternative techniques; Association analysis : basic concepts and algorithms; Association analysis : advanced concepts; Cluster analysis : basic concepts and algorithms; Cluster analysis : additional issues and algorithms; Anomaly detection; Avoiding false discoveries; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=22088

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Barcode	Call number	Media type	Location	Section	Status
1702-002536	006.312 Tan-Int 2019	General Collection	SEEU Library Skopje	English	Available

Spark / Bill Chambers

No review available on this record. Title : Spark : the definitive guide: big data processing made simple Material Type: printed text Authors: Bill Chambers, Author ; Matei Zaharia, Author Edition statement: 1st edition Publisher: Sebastopol, CA : O'Reilly & Associates Publication Date: 2018 Pagination: xxvi, 576 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-491-91221-8 General note: Includes index Languages : English (eng) Original Language : English (eng) Descriptors: Computer programs Data mining Electronic data processing Spark (Electronic resource - Apache Software Foundation) Telecommunication - Message processing. Web applications - Development Web servers - Computer programs Class number: 006.312 Abstract: "Spark : The Definitive Guide"- Learn how tu use, deploy, and maintain Apache Spark with this comprehensive guide, written by some of the creators of this open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.O, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLIib, Spark’s scalable machine learning library. -Get a gentle overview of big data and Spark -Learn about DataFrames, SQL, and Datasets-Spark core APIs-through worked examples -Dive into Spark’s low-level APIs, RDDs, and execution od SQL and DataFarmes -Understand how Spark runs on a cluster -Debug, monitor, and tune Spark clusters and applications -Learn the power of Spark’s Structured Streaming, Sparks’s stream processing engine -Learn about MLIib and how you can apply it to a variety of problems including cllasification or recommendation Contents note: Gentle overview of big data and Spark. What is Apache Spark?; A gentle introduction to Spark; A tour of Spark's toolset; Structured APIs : DataFrames, SQL, and datasets. Structured API overview; Basic structured operations; Working with different types of data; Aggregations; Joins; Data sources; Spark SQL; Datasets; Low-level APIs. Resilient distributed datasets (RDDs); Advanced RDDs; Distributed shared variables; Production applications. How Spark runs on a cluster; Developing Spark applications; Deploying Spark; Monitoring and debugging; Performance tuning; Streaming. Stream processing fundamentals; Structured streaming basics; Event-time and stateful processing; Structured streaming in production; Advanced analytics and machine learning. Advanced analytics and machine learning overview; Preprocessing and feature engineering; Classification; Regression; Recommendation; Unsupervised learning; Graph analytics; Deep learning; Ecosystem. Language specifics : Python (PySpark) and R (SparkR and sparklyr); Ecosystem and community; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17938

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Barcode	Call number	Media type	Location	Section	Status
1702-002280	006.312 Cha-Spa 2018	General Collection	Library "Max van der Stoel"	English	Available
1702-002276	006.312 Cha-Spa 2018	General Collection	SEEU Library Skopje	English	Available