English | April 24th, 2018 | ISBN: 1788393724 | 570 Pages | EPUB
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.

Key Features
Load, wrangle, and analyze your data using R - the world's most powerful statistical programming language
Gain a deeper understanding of fundamentals of applied statistics and implement them using practical use-cases
A comprehensive guide specially designed to take your understanding of R for data analysis to a new level

Book Description
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 learn
Navigate the R environment
Describe and visualize the behavior of data and relationships between data
Gain a thorough understanding of statistical reasoning and sampling
Employ hypothesis tests to draw inferences from your data
Learn Bayesian methods for estimating parameters
Use bootstrapping and an alternative to parametric hypothesis testing
Perform regression to predict continuous variables
Apply powerful classification methods to predict categorical data
Perform time series forecasting with Exponential Smoothing methods
Handle missing data gracefully using multiple imputation
Identify and manage problematic data points
Use regular expressions to clean data sets
Employ parallelization and Rcpp to scale your analyses to larger data
Put best practices into effect to make your job easier and facilitate reproducibility