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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition - Second Edition

3.5 (2)
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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition

3.5 (2)

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (19 chapters)
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Advanced topics


Linear models are the biggest idea in applied statistics and predictive analytics. There are massive volumes written about even the smallest details of linear regression. As such, there are some important ideas that we can't go over here because of space concerns, or because it requires knowledge beyond the scope of this book. So you don't feel like you're in the dark, though, here are some of the topics we didn't cover—that I would have liked to—and why they are neat:

  • Regularization: Regularization was mentioned briefly in the subsection about balancing bias and variance. In this context, regularization is a technique wherein we penalize models for complexity, to varying degrees. My favorite method of regularizing linear models is by using elastic-net regression. It is a fantastic technique and, if you are interested in learning more about it, I suggest you install and read the vignette of the glmnet package, with the help of the following code:
         install.packages(...
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Data Analysis with R, Second Edition
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