Data Analysis with R - Second Edition

More Information
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
About

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.

Features
  • Analyze your data using R – the most powerful statistical programming language
  • Learn how to implement applied statistics using practical use-cases
  • Use popular R packages to work with unstructured and structured data
Page Count 570
Course Length 17 hours 6 minutes
ISBN 9781788393720
Date Of Publication 27 Mar 2018

Authors

Tony Fischetti

Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory.

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Tony enjoys writing and contributing to open source software, blogging at onthelambda, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples.

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The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.