Overview
- 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)
Keywords
About this book
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
About the author
Bibliographic Information
Book Title: Beginning Data Science in R
Book Subtitle: Data Analysis, Visualization, and Modelling for the Data Scientist
Authors: Thomas Mailund
DOI: https://doi.org/10.1007/978-1-4842-2671-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Thomas Mailund 2017
eBook ISBN: 978-1-4842-2671-1Published: 09 March 2017
Edition Number: 1
Number of Pages: XXVII, 352
Number of Illustrations: 100 b/w illustrations
Topics: Big Data, Programming Languages, Compilers, Interpreters