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Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data
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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).
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Product details
Paperback: 506 pages
Publisher: Packt Publishing (July 29, 2016)
Language: English
ISBN-10: 1786466457
ISBN-13: 978-1786466457
Product Dimensions:
7.5 x 1.1 x 9.2 inches
Shipping Weight: 2.1 pounds (View shipping rates and policies)
Average Customer Review:
4.8 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#416,580 in Books (See Top 100 in Books)
I really enjoyed this book. Every concept is thoroughly explained, and the comparisons with other programs and platforms are really helpful. I don’t think there’s a better resource to learn R for data analytics (I’ve definitely looked and have been disappointed many, many times), so I can absolutely recommend this book.
It was exactly what I was looking for: a pedagogical read guiding me towards the next step with R. I'm an intermediate-level R user (not my first programming language), working mainly with relational databases and non-big data volumes. This book gave me a clear view of what is possible with R once the data become larger, analysis is moved to the cloud and a big-data environment (Hadoop & co) comes into play.
This is an excellent book. It is so from the purely technical point of view but it is also full of interesting facts and historical notes that make it entertaining as well. Well written and pedagogical, I definitely recommend it for anyone also looking for the R- Big Data connection.
Big data analysis has been a trend in Korea too! This book solved my curiosity of big data analysis and the practice with R.
The book starts with the good explanations of the concepts of big data, important terminologies and tools like Hadoop, MapReduce, SQL, SPARK. Then it reviews the R programing and its essential functions for data managing such as importing and exporting data, exploratory data analysis, data visualization. It continues with presenting major restrictions of R in dealing with big data and showing how to employ ff, ffbase and bigmemory packages in R to remove these limitations. Next it enters to more advanced topics Hadoop and Haddop Distributed File System (HDFS) and shows how to do data processing in Hadoop using R. It elaborates the creating single-node and multi-node Hadoop cluster with R along with the practical example. The two chapters of book are totally about connectivity of R and Relational Database Management Systems, SQL database and non-relational databases, NoSQL databes. The other chapter deals with Spark and shows how we can use it for a faster big data processing with using SparkR package in R. It also elaborates how to apply the machine learning algorithms on big data by using H2O platform and h2o package in R.In brief the book is suitable for those readers are going to start dealing with big data using R programming. It includes elementary and advanced topics of the subject successfully but it could be more useful if it includes one or two complete practical projects in details.
Great book for anyone wishing to hone their data engineering skills, having the R platform as their basis. Although this book is intended for the intermediate data science professional, some of the concepts it covers are fairly advanced, yet explained in such a way that they seem elementary. Some experience with the Linux OS (particularly the CLI aspect of it) would be very useful as the author goes into the nitty-gritty often, in order to perform low-level operations that enable the data engineering tasks he describes. He also provides a plethora of meticulously illustrated examples that make all the concepts he introduces hands-on and comprehensible.R is not my platform of choice (in fact I rarely use it nowadays), but I still enjoyed reading this book at least twice and I would recommend it to anyone who wishes to advance their data science expertise, using this particular platform. This is not a book you would read on your commute though. For best results I would recommend you treat it like a textbook and follow all of the examples on your computer.Disclaimer: I have been given a copy of this book for free in order to provide the Amazon community with an unbiased review of it. So, even though I'm not a verified purchaser, trust me when I say it, I know this book inside-out!
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