Mastering Data Analysis with R, by Gergely Daroczi
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Mastering Data Analysis with R, by Gergely Daroczi
Free Ebook Mastering Data Analysis with R, by Gergely Daroczi
Gain sharp insights into your data and solve real-world data science problems with R―from data munging to modeling and visualization
About This Book
- Handle your data with precision and care for optimal business intelligence
- Restructure and transform your data to inform decision-making
- Packed with practical advice and tips to help you get to grips with data mining
Who This Book Is For
If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic.
What You Will Learn
- Connect to and load data from R's range of powerful databases
- Successfully fetch and parse structured and unstructured data
- Transform and restructure your data with efficient R packages
- Define and build complex statistical models with glm
- Develop and train machine learning algorithms
- Visualize social networks and graph data
- Deploy supervised and unsupervised classification algorithms
- Discover how to visualize spatial data with R
In Detail
R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently.
This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage.
Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods.
Style and approach
Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.
Mastering Data Analysis with R, by Gergely Daroczi- Amazon Sales Rank: #1179834 in Books
- Published on: 2015-09-30
- Released on: 2015-09-30
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .90" w x 7.50" l, 1.49 pounds
- Binding: Paperback
- 380 pages
About the Author
Gergely Daroczi
Gergely Daroczi is a former assistant professor of statistics and an enthusiastic R user and package developer. He is the founder and CTO of an R-based reporting web application at http://rapporter.net and a PhD candidate in sociology. He is currently working as the lead R developer/research data scientist at https://www.card.com/ in Los Angeles. Besides maintaining around half a dozen R packages, mainly dealing with reporting, Gergely has coauthored the books Introduction to R for Quantitative Finance and Mastering R for Quantitative Finance (both by Packt Publishing) by providing and reviewing the R source code. He has contributed to a number of scientific journal articles, mainly in social sciences but in medical sciences as well.
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Most helpful customer reviews
3 of 3 people found the following review helpful. I found "Mastering Data Analysis with R" useful & very readable By DWR I found "Mastering Data Analysis with R" useful & very readable. I especially liked the sections dealing with obtaining, filtering & manipulating data. Though some of this information can be found in various R package vignettes, much of it, including useful tips, is all in one place here. For example, I found the section on using functions from the R package "dplyr" useful for merging data sets. The reader of this book might do well to follow-up this volume with Hadley Wickham’s "Advanced R". I also enjoyed the treatment given to importing the corpus for text analysis. Python generally seems to be a bit easier to navigate for the pre-processing step when performing text analytics. This book made conducting the process in R much easier for me.
2 of 3 people found the following review helpful. Pass By Dimitri Shvorob Packt's conveyor is not slowing down: only three months ago I surveyed their fresh crop of "data science with R" offerings"Mastering Predictive Analytics with R" by Forte, £32.99"Mastering Machine Learning with R" by Lesmeister, £34.99"R Data Analysis Cookbook" by Viswanathan and Viswanathan, £29.99"Machine Learning with R Cookbook" by Yu-Wei, £30.99and now there are four more:"Unsupervised Learning with R" by Pacheco, £25.99"Data Analysis with R" by Fischetti, £34.99"Learning Predictive Analytics with R" by Mayor, £31.99"Mastering Data Analysis with R" by Daroczi, £34.99Fischetti's book is idiosyncratic but good - that one, plus the titles by Forte and Lantz, are the only Packt offerings I like - Pacheco's and Mayor's are bad.Daroczi's is ... also bad. The book starts out with unpolished but original, obviously-speaking-from-experience and useful survey of data-access (think databases, files and URLs) and data-manipulation tools (think "sqldf", "plyr" and "data.table" packages). If all of the book's content were like that, I would give it four stars in a jiffy. Unfortunately, after a hundred pages, attention moves from data manipulation to modeling, and here "Mastering Data Analysis with R" loses edge, and joins the uninspiring ranks of low-quality, superficial "data science lite" books from Packt. Forte and Lantz are two Packt authors who have done this far, far better; outside of Packt's hit-and-miss-and-miss-and-miss-and-miss universe, you have high-quality R-illustrated books by Witten et al. (that's "Introduction to Statistical Learning"), or by Kabacoff, or by Crowley.Finally, the book mentions data visualization; here, the relevant content includes a look at GIS - OK, these several pages are duly noted, but I think I could find as much over a Google session - and a look at "igraph" package, which struck me as equally low-value-added.Summing up, I see a hundred interesting pages, and two hundred uninteresting ones. The book is hard to recommend.PS. If you see any odd things in "Comments", that's probably the psycho who has spent days posting offensive comments to my reviews - only for them to be deleted by Amazon - after I criticized an iffy Packt book promoted with questionable five-star reviews. ("R Machine Learning By Example" by Bali and Sarkar, if you are wondering). As long as you know that his "Dimitri Shvorob" is fake, there isn't much harm he can do.
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