EBSCOhost
World Bank
EconBiz
De Gruyter
OpenEdition
European Respiratory Journal
Mathematical Sciences Publishers Journals
Edward Elgar Publishing UN iLibrary HeinOnline Directory of Open Access Books SAGE Journals ASTM Compass
From this page you can:
| Home |
Descriptors
> Big data 
Make a suggestion Refine your searchBig data analytics with R / Simon Walkowiak
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 -- SoftwareClass 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 Hold
Place a hold on this item
Copies
Barcode Call number Media type Location Section Status 1702-002377 006.312 Wal-Big 2016 General Collection SEEU Library Skopje English Due for return by 05/31/2022
Title : Big data, data mining and machine learning : value creation for business leaders and practitioners Material Type: printed text Authors: Jared Dean, Author Publisher: Hoboken, N.J. : J. Wiley & Sons Publication Date: 2014 Pagination: xix, 265 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-11-861804-2 General note: Includes bibliographical references and index Languages : English (eng) Original Language : English (eng) Descriptors: Big data
Data mining
Database management
Information technology - Management
Management - Data processingClass number: 658.05 Abstract: An expert guide to high performance computing architectures and how they relate to analytics and data miningWith the exponential growth of data comes an ever-increasing need to process and analyze so-called Big Data. High Performance Data Mining and Big Data Analytics provides a comprehensive view of the recent trend toward high performance computing architectures and its natural connection to analytics and data mining. You'll find coverage of topics including: big data, high performance computing for analytics, massively parallel processing (MPP) databases, in-memory analytics, implementation of machine learning algorithms for big data platforms, text analytics, analytics environments, the analytics lifecycle, general applications, as well as a variety of cases. Offers coverage of business analytics, predictive modeling, and fact-based management Includes case studies featuring multinational companies Explores recent trends in high performance computing architectures relating to data mining Filled with case studies, High Performance Data Mining and Big Data Analytics provides a thorough grounding for optimally putting data mining and big data analytics to work for your organization". Contents note: Introduction; The Computing Environment; Hardware; Distributed Systems; Analytical Tools; Turning Data into Business Value; Predictive Modeling; Common Predictive Modeling Techniques; Segmentation; Incremental Response Modeling; Time Series Data Mining; Recommendation Systems; Text Analytics; Success Stories of Putting it all Together; Case Study of a Large U.S.‐Based Financial Services Company; Case Study of a Major Health Care Provider; Case Study of a Technology Manufacturer; Case Study of Online Brand Management; Case Study of Mobile Application Recommendations; Case Study of a High‐Tech Product Manufacturer; Looking to the Future; Free Access; Appendix; References; Index; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17947 Hold
Place a hold on this item
Copies
Barcode Call number Media type Location Section Status 1702-002269 658.05 Dea-Big 2014 General Collection SEEU Library Skopje English Available
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 Hold
Place a hold on this item
Copies
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
Title : Practical Hadoop Ecosystem : a definitive guide to Hadoop-related frameworks and tools Material Type: printed text Authors: Deepak Vohra, Author Publisher: New York : Apress Publication Date: 2016 Pagination: xx, 421 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-484-22198-3 General note: Includes index (p. 415-421) Languages : English (eng) Original Language : English (eng) Descriptors: Big data
Computer science
Database management - Computer programsClass number: 005.74 Abstract: "Practical Hadoop Ecosystem"- is a practical guide on using the Apache Hadoop projects including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout and Apache Solr. From setting up the environment to running sample applications each chapter is a practical tutorial on using a Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects MapReduce and HDFS and none discusses the other Apache Hadoop ecosystem projects and how these all work together as a cohesive big data development platform. What you'll learn How to set up environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5. How to run a MapReduce job How to store data with Apache Hive, Apache HBase How to index data in HDFS with Apache Solr How to develop a Kafka messaging system How to develop a Mahout User Recommender System How to stream Logs to HDFS with Apache Flume How to transfer data from MySQL database to Hive, HDFS and HBase with Sqoop How create a Hive table over Apache Solr. Contents note: HDFS and MapReduce; Apache Hive; Apache HBase; Apache Sqoop; Apache Flume; Apache Avro; Apache Parquet; Apache Kafka; Apache Solr; Apache Mahout; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17952 Hold
Place a hold on this item
Copies
Barcode Call number Media type Location Section Status 1702-002267 005.74 Voh-Pra 2016 General Collection SEEU Library Skopje English Available
