Beginning Data Science in R 4
eBook - ePub

Beginning Data Science in R 4

Data Analysis, Visualization, and Modelling for the Data Scientist

  1. English
  2. ePUB (mobile friendly)
  3. Only available on web
eBook - ePub

Beginning Data Science in R 4

Data Analysis, Visualization, and Modelling for the Data Scientist

Book details
Table of contents
Citations

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. 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.

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Yes, you can access Beginning Data Science in R 4 by Thomas Mailund in PDF and/or ePUB format, as well as other popular books in Computer Science & Compilers. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Apress
Year
2022
ISBN
9781484281550
Edition
2
Topic
Computer Science
Subtopic
Compilers
Index
Computer Science

Table of contents

  1. Cover
  2. Front Matter
  3. 1. Introduction to R Programming
  4. 2. Reproducible Analysis
  5. 3. Data Manipulation
  6. 4. Visualizing Data
  7. 5. Working with Large Data Sets
  8. 6. Supervised Learning
  9. 7. Unsupervised Learning
  10. 8. Project 1: Hitting the Bottle
  11. 9. Deeper into R Programming
  12. 10. Working with Vectors and Lists
  13. 11. Functional Programming
  14. 12. Object-Oriented Programming
  15. 13. Building an R Package
  16. 14. Testing and Package Checking
  17. 15. Version Control
  18. 16. Profiling and Optimizing
  19. 17. Project 2: Bayesian Linear Regression
  20. Back Matter