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Beginning Data Science with R
“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library.
The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the specific processes and methodologies, and understanding the going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the specific processes and methodologies, and understanding the going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
168 pages, Hardcover
First published November 28, 2014
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February 4, 2015
Continuing on with the Springer series on Computational Intelligence and Complexity I picked another book on the ever increasing in popularity R.
Besides, I read already several books from other publishers in 2014. The books were aiming at different levels, and at people from different professional backgrounds. Myself, a data practitioner, positioned rather away from being a data scientist, sitting closer to the server side, with periodic ETL or Business Intelligence development tasks at hand professional I started to realize the times have changed: each new project requires new depth and breaths of data analysis. Using Excel and its data add-ons does no longer cut it in. I was aware of tools as MATLAB, SAS and SPSS, but boy they cost!
I was always in love with data, linear, discrete algebra and statistics in general so for me R came to the natural choice. Learning tools as R (not just a new language) is quite an endeavour so I resorted to the World Wisde Web in search for good programming reference for R and was able to encounter numerous posts with recommendations. Then every book I picked did not quite stood to its promises. I was in despair.
But no longer, now when I found Beginning Data Science with R I feel empowered.
I will try to outline why this book does deliver.
All the beginner books I tossed into my tablet expected the reader to have an advanced knowledge in statistics. And far less in R. This is an ill conceived approach to me. In fact, R is a complex language with MANY nuances (not quirks IMO). Yet, there are dozens of ways to arrive to the same thing, like in Perl, but don't let me get started...
The main idea I try to convey is that there was never a book with a good mix or balance per-se between R as a language and where it fits or excels (or delivers) when it comes to statistics or probalistics. This book does. It is very well-rounded. A reader from each level will find much useful information. So I don't necessarily consider this book a beginner one. It has many reference links a reader can utilize to widen one's knowledge. And I had so much fun reading this not terribly long book! All the topics are explained very well, with enough intro and concrete examples. Some chapters I see as a bonus, especially the one on text mining (which some from my G+ post do not consider a part of R) and decision trees. These I liked the most, they are poor fun, short, but very practical. Not to mention useful.
To sum up,
What this book is: a comprehensive, yet short tutorial on practical application of R to the modern data science tasks or projects. The book lays a solid foundation to develop your knowledge in R further on, a good guide for what is possible to extract from R and its CRAN (and the packages ecosystem) or even computational and quantitative science itself. Perhaps this book helps in grasping with Machine Learning as well, and other advanced areas of the Data Science.
What this book is not: a reader would need supplementary material to delve deeper into R as a language and may need extra practice on concrete or narrowed down, specific tasks/applications.
Who I recommend it to: managers who work on data projects, technical team leaders, CS students, Business Intelligence professionals, beginner architects, general computer academia, statisticians, several categories of scientists or researchers as biologists, lab, criminologists, and also Finance pros or actuarials.
Verdict: 5 out of 5, well done Mr Manas A. Pathak!
Besides, I read already several books from other publishers in 2014. The books were aiming at different levels, and at people from different professional backgrounds. Myself, a data practitioner, positioned rather away from being a data scientist, sitting closer to the server side, with periodic ETL or Business Intelligence development tasks at hand professional I started to realize the times have changed: each new project requires new depth and breaths of data analysis. Using Excel and its data add-ons does no longer cut it in. I was aware of tools as MATLAB, SAS and SPSS, but boy they cost!
I was always in love with data, linear, discrete algebra and statistics in general so for me R came to the natural choice. Learning tools as R (not just a new language) is quite an endeavour so I resorted to the World Wisde Web in search for good programming reference for R and was able to encounter numerous posts with recommendations. Then every book I picked did not quite stood to its promises. I was in despair.
But no longer, now when I found Beginning Data Science with R I feel empowered.
I will try to outline why this book does deliver.
All the beginner books I tossed into my tablet expected the reader to have an advanced knowledge in statistics. And far less in R. This is an ill conceived approach to me. In fact, R is a complex language with MANY nuances (not quirks IMO). Yet, there are dozens of ways to arrive to the same thing, like in Perl, but don't let me get started...
The main idea I try to convey is that there was never a book with a good mix or balance per-se between R as a language and where it fits or excels (or delivers) when it comes to statistics or probalistics. This book does. It is very well-rounded. A reader from each level will find much useful information. So I don't necessarily consider this book a beginner one. It has many reference links a reader can utilize to widen one's knowledge. And I had so much fun reading this not terribly long book! All the topics are explained very well, with enough intro and concrete examples. Some chapters I see as a bonus, especially the one on text mining (which some from my G+ post do not consider a part of R) and decision trees. These I liked the most, they are poor fun, short, but very practical. Not to mention useful.
To sum up,
What this book is: a comprehensive, yet short tutorial on practical application of R to the modern data science tasks or projects. The book lays a solid foundation to develop your knowledge in R further on, a good guide for what is possible to extract from R and its CRAN (and the packages ecosystem) or even computational and quantitative science itself. Perhaps this book helps in grasping with Machine Learning as well, and other advanced areas of the Data Science.
What this book is not: a reader would need supplementary material to delve deeper into R as a language and may need extra practice on concrete or narrowed down, specific tasks/applications.
Who I recommend it to: managers who work on data projects, technical team leaders, CS students, Business Intelligence professionals, beginner architects, general computer academia, statisticians, several categories of scientists or researchers as biologists, lab, criminologists, and also Finance pros or actuarials.
Verdict: 5 out of 5, well done Mr Manas A. Pathak!
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