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About this item
Highlights
- Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist.
- About the Author: Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark.
- 511 Pages
- Computers + Internet, Programming Languages
Description
Book Synopsis
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.
From the Back Cover
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
About the Author
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.Dimensions (Overall): 10.0 Inches (H) x 7.0 Inches (W) x 1.09 Inches (D)
Weight: 2.04 Pounds
Suggested Age: 22 Years and Up
Sub-Genre: Programming Languages
Genre: Computers + Internet
Number of Pages: 511
Publisher: Apress
Theme: General
Format: Paperback
Author: Thomas Mailund
Language: English
Street Date: June 24, 2022
TCIN: 1003044056
UPC: 9781484281543
Item Number (DPCI): 247-49-8774
Origin: Made in the USA or Imported
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Shipping details
Estimated ship dimensions: 1.09 inches length x 7 inches width x 10 inches height
Estimated ship weight: 2.04 pounds
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