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Data Analysis with R - A comprehensive guide to manipulating, analyzing, and visualizing data in R, 2nd Edition (Paperback, 2nd Revised edition)
Loot Price: R1,155
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Data Analysis with R - A comprehensive guide to manipulating, analyzing, and visualizing data in R, 2nd Edition (Paperback, 2nd Revised edition)
Expected to ship within 18 - 22 working days
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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. Key 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
Book 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 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. 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 Who this book is forBudding
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.
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