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Beginning data science in R 4 = data...

Mailund, Thomas.

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Beginning data science in R 4 = data analysis, visualization, and modelling for the data scientist /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginning data science in R 4/ by Thomas Mailund.
Reminder of title:	data analysis, visualization, and modelling for the data scientist /
Author:	Mailund, Thomas.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xxviii, 511 p. :ill., digital ;24 cm.
[NT 15003449]:	1: Introduction -- 2: Introduction to R Programming -- 3: Reproducible Analysis -- 4: Data Manipulation -- 5: Visualizing Data -- 6: Working with Large Data Sets -- 7: Supervised Learning -- 8: Unsupervised Learning -- 9: Project 1: Hitting the Bottle -- 10: Deeper into R Programming -- 11: Working with Vectors and Lists -- 12: Functional Programming -- 13: Object-Oriented Programming -- 14: Building an R Package -- 15: Testing and Package Checking -- 16: Version Control -- 17: Profiling and Optimizing -- 18: Project 2: Bayesian Linear Progression -- 19: Conclusions.
Contained By:	Springer Nature eBook
Subject:	R (Computer program language) -
Online resource:	https://doi.org/10.1007/978-1-4842-8155-0
ISBN:	9781484281550

Beginning data science in R 4 = data analysis, visualization, and modelling for the data scientist /
Mailund, Thomas.

Beginning data science in R 4data analysis, visualization, and modelling for the data scientist /[electronic resource] :by Thomas Mailund. - Second edition. - Berkeley, CA :Apress :2022. - xxviii, 511 p. :ill., digital ;24 cm.

1: Introduction -- 2: Introduction to R Programming -- 3: Reproducible Analysis -- 4: Data Manipulation -- 5: Visualizing Data -- 6: Working with Large Data Sets -- 7: Supervised Learning -- 8: Unsupervised Learning -- 9: Project 1: Hitting the Bottle -- 10: Deeper into R Programming -- 11: Working with Vectors and Lists -- 12: Functional Programming -- 13: Object-Oriented Programming -- 14: Building an R Package -- 15: Testing and Package Checking -- 16: Version Control -- 17: Profiling and Optimizing -- 18: Project 2: Bayesian Linear Progression -- 19: Conclusions.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code.

ISBN: 9781484281550

Standard No.: 10.1007/978-1-4842-8155-0doiSubjects--Topical Terms:

784593
R (Computer program language)

LC Class. No.: QA276.45.R3 / M35 2022

Dewey Class. No.: 519.50285536

Beginning data science in R 4 = data analysis, visualization, and modelling for the data scientist /
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505 0 $a 1: Introduction -- 2: Introduction to R Programming -- 3: Reproducible Analysis -- 4: Data Manipulation -- 5: Visualizing Data -- 6: Working with Large Data Sets -- 7: Supervised Learning -- 8: Unsupervised Learning -- 9: Project 1: Hitting the Bottle -- 10: Deeper into R Programming -- 11: Working with Vectors and Lists -- 12: Functional Programming -- 13: Object-Oriented Programming -- 14: Building an R Package -- 15: Testing and Package Checking -- 16: Version Control -- 17: Profiling and Optimizing -- 18: Project 2: Bayesian Linear Progression -- 19: Conclusions.
520 $a Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code.
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