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Beginning Data Science with R electronic resource by Manas A. Pathak.

By: Pathak, Manas A [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextPublication details: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XI, 157 p. 155 illus., 26 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319120669Subject(s): engineering | Mathematical statistics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Statistics and Computing/Statistics Programs | Signal, Image and Speech ProcessingDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Contents:
Introduction -- Overview of the R Programming Language -- Getting Data into R -- Data Visualization -- Exploratory Data Analysis -- Regression -- Classification -- Text Mining.  .
In: Springer eBooksSummary: “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.
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Introduction -- Overview of the R Programming Language -- Getting Data into R -- Data Visualization -- Exploratory Data Analysis -- Regression -- Classification -- Text Mining.  .

“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.

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