Beginning Data Science with R (PDF)
(Sprache: Englisch)
"Data Science with R" deals with implementing many useful data analysis methodologies with the R programming language. The target audience for this book is non-R programmers and non-statisticians. The book will cover all the necessary concepts from the...
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"Data Science with R" deals with implementing many useful data analysis methodologies with the R programming language. The target audience for this book is non-R programmers and non-statisticians. The book will cover all the necessary concepts from the basics to state-of-the-art technologies like working with big data. The author attempts to strike a balance between the "how": specific processes and methodologies, while also talking about the "why": giving an intuition behind how a particular technique works, so that the reader can apply the generalized solution to the problem at hand.
Autoren-Porträt von Manas A. Pathak
Dr. Manas A. Pathak received a BTech degree in computer science from Visvesvaraya National Institute of Technology, Nagpur, India, in 2006, and MS and PhD degrees from the Language Technologies Institute at Carnegie Mellon University (CMU) in 2009 and 2012 respectively. His PhD thesis on "Privacy-Preserving Machine Learning for Speech Processing" was published as a monograph in the Springer best thesis series. His research received significant press coverage, including articles in the Economist and MIT Tech Review. He has many years of experience with data analysis using the R programming language. He is currently working as a staff software engineer at @WalmartLabs.
Bibliographische Angaben
- Autor: Manas A. Pathak
- 2014, 2014, 157 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319120662
- ISBN-13: 9783319120669
- Erscheinungsdatum: 08.12.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Dateiformat: PDF
- Größe: 3.90 MB
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Sprache:
Englisch
Pressezitat
“The target audience for this book is non-R programmers and non-statisticians. … if you want to get started with R and/or new statistical procedures have a look at this book. It can be quite helpful.” (David E. Booth, Technometrics, Vol. 58 (2), 2016)“This book is written for coders who already know how to code to learn R for data science. The book covers how to install and use R … . This is a good book to get you stated coding in R for data science.” (Mary Anne, Cats and Dogs with Data, maryannedata.com, May, 2015)
“A comprehensive, yet short tutorial on practical application of R to the modern data science tasks or projects. … 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.” (Compudicted, compudicted.wordpress.com, February, 2015)
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