R for Data Science

Learn and explore the fundamentals of data science with R

R for Data Science

Dan Toomey

7 customer reviews
Learn and explore the fundamentals of data science with R
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Book Details

ISBN 139781784390860
Paperback364 pages

Book Description

R is a powerful, open source, functional programming language. It can be used for a wide range of programming tasks and is best suited to produce data and visual analytics through customizable scripts and commands.

The purpose of the book is to explore the core topics that data scientists are interested in. This book draws from a wide variety of data sources and evaluates this data using existing publicly available R functions and packages. In many cases, the resultant data can be displayed in a graphical form that is more intuitively understood. You will also learn about the often needed and frequently used analysis techniques in the industry.

By the end of the book, you will know how to go about adopting a range of data science techniques with R.

Table of Contents

Chapter 2: Data Mining Sequences
Chapter 3: Text Mining
Chapter 4: Data Analysis – Regression Analysis
Chapter 5: Data Analysis – Correlation
Chapter 6: Data Analysis – Clustering
Chapter 7: Data Visualization – R Graphics
Chapter 10: Machine Learning in Action
Chapter 11: Predicting Events with Machine Learning
Chapter 12: Supervised and Unsupervised Learning

What You Will Learn

  • Develop, execute, and modify R scripts
  • Find, install, and use third-party R packages
  • Organize your data to get the best results
  • Produce graphical displays of your results, including 3D visualizations
  • Perform statistical analyses that you can use all the time
  • Understand the trade-offs between different approaches to problems
  • Be comfortable with trying features to fine-tune your results
  • Adopt and learn data science with R in a practical tutorial format
  • Explore concepts such as data mining, data analysis, data visualization, and machine learning using R

Authors

Table of Contents

Chapter 2: Data Mining Sequences
Chapter 3: Text Mining
Chapter 4: Data Analysis – Regression Analysis
Chapter 5: Data Analysis – Correlation
Chapter 6: Data Analysis – Clustering
Chapter 7: Data Visualization – R Graphics
Chapter 10: Machine Learning in Action
Chapter 11: Predicting Events with Machine Learning
Chapter 12: Supervised and Unsupervised Learning

Book Details

ISBN 139781784390860
Paperback364 pages
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