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

✍ Scribed by Thomas Mailund (auth.)

• Publisher: Apress
• Year: 2017
• Tongue: English
• Leaves: 369
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxvii
Introduction to R Programming....Pages 1-28
Reproducible Analysis....Pages 29-44
Data Manipulation....Pages 45-73
Visualizing Data....Pages 75-111
Working with Large Datasets....Pages 113-124
Supervised Learning....Pages 125-167
Unsupervised Learning....Pages 169-204
More R Programming....Pages 205-231
Advanced R Programming....Pages 233-256
Object Oriented Programming....Pages 257-267
Building an R Package....Pages 269-280
Testing and Package Checking....Pages 281-286
Version Control....Pages 287-301
Profiling and Optimizing....Pages 303-346
Back Matter....Pages 347-352

✦ Subjects

Data Mining and Knowledge Discovery;Big Data;Programming Languages, Compilers, Interpreters;Data-driven Science, Modeling and Theory Building;Programming Techniques