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Beginning Data Science in R

Data Analysis, Visualization, and Modelling for the Data Scientist

  • Book
  • © 2017

Overview

Authors:
  1. Thomas Mailund

    1. Aarhus, Denmark

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  • Gives you everything you need to know to get started in data science and R programming
  • A unique book by a data science expert
  • Based on a successful lecture series

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxvii
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  2. Introduction to R Programming

    • Thomas Mailund
    Pages 1-28

  3. Reproducible Analysis

    • Thomas Mailund
    Pages 29-44

  4. Data Manipulation

    • Thomas Mailund
    Pages 45-73

  5. Visualizing Data

    • Thomas Mailund
    Pages 75-111

  6. Working with Large Datasets

    • Thomas Mailund
    Pages 113-124

  7. Supervised Learning

    • Thomas Mailund
    Pages 125-167

  8. Unsupervised Learning

    • Thomas Mailund
    Pages 169-204

  9. More R Programming

    • Thomas Mailund
    Pages 205-231

  10. Advanced R Programming

    • Thomas Mailund
    Pages 233-256

  11. Object Oriented Programming

    • Thomas Mailund
    Pages 257-267

  12. Building an R Package

    • Thomas Mailund
    Pages 269-280

  13. Testing and Package Checking

    • Thomas Mailund
    Pages 281-286

  14. Version Control

    • Thomas Mailund
    Pages 287-301

  15. Profiling and Optimizing

    • Thomas Mailund
    Pages 303-346

  16. Back Matter

    Pages 347-352
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Keywords

About this book

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.



Authors and Affiliations

  • Aarhus, Denmark

    Thomas Mailund

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.


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