Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist - Softcover

Mailund, Thomas

 
9781484281543: Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist
Softcover
ISBN 10: 1484281543 ISBN 13: 9781484281543
Verlag: Apress, 2022

Inhaltsangabe

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. 

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 github.com/Apress/beg-data-science-r4.


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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. 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.

Von der hinteren Coverseite

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

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Apress
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
1484281543
ISBN 13 
9781484281543
Einband
Taschenbuch
Auflage
2
Anzahl der Seiten
540
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