Data Analysis with R - Second Edition

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use.

Data Analysis with R - Second Edition

Tony Fischetti

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use.
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Book Details

ISBN 139781788393720
Paperback570 pages

Book Description

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

What You Will Learn

  • Gain a thorough understanding of statistical reasoning and sampling theory
  • Employ hypothesis testing to draw inferences from your data
  • Learn Bayesian methods for estimating parameters
  • Train regression, classification, and time series models
  • Handle missing data gracefully using multiple imputation
  • Identify and manage problematic data points
  • Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization
  • Put best practices into effect to make your job easier and facilitate reproducibility

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Table of Contents

Book Details

ISBN 139781788393720
Paperback570 pages
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