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Cover: 9781484281543 | Beginning Data Science in R 4 | Thomas Mailund | Taschenbuch | xxviii
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Beginning Data Science in R 4
Data Analysis, Visualization, and Modelling for the Data Scientist
Taschenbuch von Thomas Mailund
Sprache: Englisch

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Beschreibung
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. Yoüll 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.
Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well.
Source code is available at [...]
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
Who This Book Is For
Those with some data science or analytics background, but not necessarily experience with the R programming language.
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. Yoüll 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.
Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well.
Source code is available at [...]
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
Who This Book Is For
Those with some data science or analytics background, but not necessarily experience with the R programming language.
Über den Autor
Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science but for the last decade his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.
Inhaltsverzeichnis
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.
Details
Medium: Taschenbuch
Inhalt: xxviii
511 S.
100 s/w Illustr.
511 p. 100 illus.
ISBN-13: 9781484281543
ISBN-10: 1484281543
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Mailund, Thomas
Auflage: 2nd ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 29 mm
Von/Mit: Thomas Mailund
Erscheinungsdatum: 24.06.2022
Gewicht: 1,003 kg
Artikel-ID: 121284760
Über den Autor
Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science but for the last decade his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.
Inhaltsverzeichnis
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.
Details
Medium: Taschenbuch
Inhalt: xxviii
511 S.
100 s/w Illustr.
511 p. 100 illus.
ISBN-13: 9781484281543
ISBN-10: 1484281543
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Mailund, Thomas
Auflage: 2nd ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 29 mm
Von/Mit: Thomas Mailund
Erscheinungsdatum: 24.06.2022
Gewicht: 1,003 kg
Artikel-ID: 121284760
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