Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data - Softcover

Walkowiak, Simon

Softcover

ISBN 10: 1786466457 ISBN 13: 9781786466457

Publisher: Packt Publishing, 2016

Synopsis

Key Features

• 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 market

Book Description

Big 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 begins with a brief introduction to the Big Data world and its current industry standards, with an introduction to the R language presenting its development, structure, applications in the real world, and its shortcomings. The book then progresses towards the revision of major R functions for data management and transformations. You'll be introduced to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters), and be given guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. In addition to this, Big Data Analytics with R expands to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks, including other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.

What you will learn

• Learn about the current state of Big Data processing using the 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 platform

About the Author

Simon Walkowiak is a cognitive neuroscientist and a managing director of Mind Project Ltd - a Big Data and Predictive Analytics consultancy based in London, United Kingdom. As a former data curator at the UK Data Service (UKDS, University of Essex) European largest socio-economic data repository, Simon has an extensive experience in processing and managing large-scale datasets such as censuses, sensor and smart meter data, telecommunication data and well-known governmental and social surveys such as the British Social Attitudes survey, Labour Force surveys, Understanding Society, National Travel survey, and many other socio-economic datasets collected and deposited by Eurostat, World Bank, Office for National Statistics, Department of Transport, NatCen and International Energy Agency, to mention just a few. Simon has delivered numerous data science and R training courses at public institutions and international companies. He has also taught a course in Big Data Methods in R at major UK universities and at the prestigious Big Data and Analytics Summer School organized by the Institute of Analytics and Data Science (IADS).

Table of Contents

1. The Era of Big Data
2. Introduction to R Programming Language and Statistical Environment
3. Unleashing the Power of R from Within
4. Hadoop and MapReduce Framework for R
5. R with Relational Database Management Systems (RDBMSs)
6. R with Non-Relational (NoSQL) Databases
7. Faster than Hadoop - Spark with R
8. Machine Learning Methods for Big Data in R
9. The Future of R - Big, Fast, and Smart Data

About this title

About the Author

Simon Walkowiak is a cognitive neuroscientist and a managing director of Mind Project Ltd - a Big Data and Predictive Analytics consultancy based in London, United Kingdom. As a former data curator at the UK Data Service (UKDS, University of Essex) - European largest socioeconomic data repository, Simon has an extensive experience in processing and managing largescale datasets such as censuses, sensor and smart meter data, telecommunication data and wellknown governmental and social surveys such as the British Social Attitudes survey, Labour Force surveys, Understanding Society, National Travel survey, and many other socioeconomic datasets collected and deposited by Eurostat, World Bank, Office for National Statistics, Department of Transport, NatCen and International Energy Agency, to mention just a few. Simon has delivered numerous data science and R training courses at public institutions and international companies. He has also taught a course in Big Data Methods in R at major UK universities and at the prestigious Big Data and Analytics Summer School organized by the Institute of Analytics and Data Science (IADS).

Edition details

• Publisher: Packt Publishing
• Publication date: 2016
• ISBN 10: 1786466457
• ISBN 13: 9781786466457
• Binding: Paperback
• Language: English
• Number of pages: 506