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A guide to manipulating, analyzing, and visualizing data in R

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. 

Learn the fundamentals of data analysis in the second edition of Data Analysis with R, authored by data scientist Tony Fischetti , along with several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use.

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.

This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.

Who this book is for:

Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.

 

What will you learn from this book:

  • 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

 

Enjoy a preview of the book here.

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Tags: Bayesian, R, analysis, analytics, data, mining, modeling, predictive, science, statistics

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