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Data Analysis with R, Second Edition: A comprehensive guide to manipulating, analyzing, and visualizing data in R, 2nd Edition-Anthony Fischetti

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Ebook About
Key FeaturesLoad, wrangle, and analyze your data using R - the world's most powerful statistical programming languageGain a deeper understanding of fundamentals of applied statistics and implement them using practical use-casesA comprehensive guide specially designed to take your understanding of R for data analysis to a new levelBook DescriptionFrequently 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. 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.What you will learnNavigate the R environmentDescribe and visualize the behavior of data and relationships between dataGain a thorough understanding of statistical reasoning and samplingEmploy hypothesis tests to draw inferences from your dataLearn Bayesian methods for estimating parametersUse bootstrapping and an alternative to parametric hypothesis testingPerform regression to predict continuous variablesApply powerful classification methods to predict categorical dataPerform time series forecasting with Exponential Smoothing methodsHandle missing data gracefully using multiple imputationIdentify and manage problematic data pointsUse regular expressions to clean data setsEmploy parallelization and Rcpp to scale your analyses to larger dataPut best practices into effect to make your job easier and facilitate reproducibility

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