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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1702-002377	006.312 Wal-Big 2016	General Collection	Library "Max van der Stoel"	English	Available

Big data, data mining and machine learning / Jared Dean

No review available on this record. 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 processing Class 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

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1702-002269	658.05 Dea-Big 2014	General Collection	Library "Max van der Stoel"	English	Due for return by 10/12/2023

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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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

Data mining for business intelligence / Shmueli Galit

No review available on this record. Title : Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner Material Type: printed text Authors: Shmueli Galit, Author ; Nitin R. Patel, Author ; Peter C. Bruce, Author Publisher: New York : John Wiley & Sons, Inc Publication Date: 2007 Pagination: xviii, 279 p. Layout: ill. Size: 26 cm. ISBN (or other code): 978-0-470-08485-4 General note: Includes bibliographical references (p. 271-272) Includes index (p. 273-279) Languages : English (eng) Original Language : English (eng) Descriptors: Business - Data processing Data mining Microsoft Excel (Computer file) Class number: 005.54 Abstract: Data mining—the art of extracting useful information from large amounts of data—is of growing importance in today's world. Your e-mail spam filter relies at least in part on rules that a data mining algorithm has learned from examining millions of e-mail messages that have been classified as spam or not-spam. Real-time data mining methods enable Web-based merchants to tell you that "customers who purchased x are also likely to purchase y." Data mining helps banks determine which applicants are likely to default on loans, helps tax authorities identify which tax returns are most likely to be fraudulent, and helps catalog merchants target those customers most likely to purchase. And data mining is not just about numbers—text mining techniques help search engines like Google and Yahoo find what you are looking for by ordering documents according to their relevance to your query. In the process they have effectively monetized search by ordering sponsored ads that are relevant to your query. The amount of data flowing from, to, and through enterprises of all sorts is enormous, and growing rapidly—more rapidly than the capabilities of organizations to use it. Successful enterprises are those that make effective use of the abundance of data to which they have access: to make better predictions, better decisions, and better strategies. The margin over a competitor may be small (they, after all, have access to the same methods for making effective use of information), hence the need to take advantage of every possible avenue to advantage. At no time has the need been greater for quantitatively skilled managerial expertise. Successful managers now need to know about the possibilities and limitations of data mining. But at what level? A high-level overview can provide a general idea of what data mining can do for the enterprise but fails to provide the intuition that could be attained by actually building models with real data. Contents note: Overview of the Data Mining Process; Data Exploration and Dimension Reduction; Evaluating Classification and Predictive Performance; Multiple Linear Regression; Classification and Regression Trees; Logistic Regression; Neural Nets; Discriminant Analysis; Association Rules; Cluster analysis; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14315

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1702-000843	005.54 Gal-Dat 2007	General Collection	Library "Max van der Stoel"	English	Available

Data mining / Ian Witten

No review available on this record. Title : Data mining : practical machine learning tools and techniques Material Type: printed text Authors: Ian Witten, Author ; Eibe Frank, Author ; Mark Hall, Author Edition statement: 4th edition Publisher: Elsevier Science & Technology Publication Date: 2016 Pagination: 654 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-0-12-804291-5 General note: Includes bibliographical references and index Languages : English (eng) Original Language : English (eng) Descriptors: Data mining Class number: 006.3 Abstract: Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects. Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods. Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface. Includes open-access online courses that introduce practical applications of the material in the book Contents note: Part I: Introduction to data mining; What's it all about?; Input: Concepts, instances, attributes; Output: Knowledge representation; Algorithms: The basic methods; Credibility: Evaluating what's been learned; More advanced machine learning schemes; Part II. More advanced machine learning schemes; Trees and rules; Extending instance-based and linear models; Data transformations; Probabilistic methods; Deep learning; Beyond supervised and unsupervised learning; Ensemble learning; Moving on: applications and beyond; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17092

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1702-002452	006.3 Wit-Dat 2016	General Collection	Library "Max van der Stoel"	English	Due for return by 03/21/2023
1702-002451	006.3 Wit-Dat 2016	General Collection	Library "Max van der Stoel"	English	Available

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