Skip to main content
Books, videos, and music - all free from your public library!
LoginSign Up

Footer

Hoopla logo, Go to homepage
  • For Patrons
  • For Libraries (opens in new window)
  • For Vendors (opens in new window)
  • Facebook (opens in new window)
  • X (opens in new window)
  • Instagram (opens in new window)
  • YouTube (opens in new window)
  • TikTok (opens in new window)
  • LinkedIn (opens in new window)

Our Company

  • Our Story
  • Get Hoopla for your Library (opens in new window)
  • Get your content on hoopla (opens in new window)
  • Join our team (opens in new window)
  • Accessibility Statement

Our Content

  • Audiobooks
  • Ebooks
  • Movies
  • Television
  • Comics
  • BingePasses
  • Music
  • The Loop Blog

Help

  • Help Center
  • Submit Feedback
  • Facebook (opens in new window)
  • X (opens in new window)
  • Instagram (opens in new window)
  • YouTube (opens in new window)
  • TikTok (opens in new window)
  • LinkedIn (opens in new window)
  • Download on the App Store (opens in new window)
  • Get it on Google Play (opens in new window)
  • Available at Amazon Appstore (opens in new window)
© 2026 Midwest Tape, LLC. All rights reserved. Privacy Policy | Terms of Use
  • Hoopla logo
    Powered by Hoopla
  • Browse
  • My Hoopla
  • Log In
  1. Navigate Home
  2. Ebooks
  3. Beginning Data Science in R

EBOOK

Beginning Data Science in R

Data Analysis, Visualization, And Modelling For The Data Scientist

Thomas MailundSeries: Professional and Applied Computing
(0)
sign up
Year
2017
Language
English
Publisher
Apress

About

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. 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 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



Who This Book Is For

Those with some data science or analytics background, but not necessarily experience with the R programming language.

Related Subjects

  • Database Administration & Management
  • Computers
  • Adult Nonfiction
  • General
  • Probability & Statistics
  • Mathematics
  • Compilers
  • Programming

Extended Details

  • SeriesProfessional and Applied Computing

    Artists

    Thomas MailundAuthor