An Introduction to Statistical Learning: with Applications in R
An Introduction to Statistical Learning offers a complete overview of the statistical field. So this book is an essential toolkit to deal with big data. Today every field is generating complex data sets ranging from finance to marketing. And biology to astrophysics for the last twenty years.
This guide presents the most important modeling and prediction methods along with different relevant applications. Moreover, this book covers several useful topics for data scientists. That includes linear regression, resampling methods, and tree-based methods. In addition to this, it explains clustering shrinking approaches. Moreover, classification, support vector machines and much more.
This book uses the best illustration to increase readability. So color graphics and real-world examples enrich this book to illustrate the methods presented. The main goal of this book to facilitate the reader by providing different statistical learning techniques. So these techniques are very useful for industries, practitioners in science and other fields. Each chapter of this book contains a tutorial on the implementation of analyses and methods presented in R. Moreover, this language is a renowned open-source statistical software platform.
An Introduction to Statistical Learning is a very useful book for machine learning researchers. Furthermore, this book targets a much broader audience. You will find many cutting-edge statistical learning tricks and techniques to analyze your data. If you want to analyze complex data with R, An Introduction to Statistical Learning is one of the finest options.
The Book of R: A First Course in Programming and Statistics
R is one of the most powerful programming languages for statistical analysis in the world. The Book of R is a detailed guide for statistical analysis. If you don’t have any prior programming experience, there is no need to worry. So this book is very beginner-friendly to R language. All you need to have a basic knowledge of mathematics.
When you start with The Book of R, you will find everything to get started effectively for statistical analysis. The book begins with the basics like how to write simple programs to handle data. When you get hands-on experience with different features, the book takes you to the advanced level. Soon you start producing detailed statistical summaries of your data after analysis. So you perform different statistical tests and modeling operations on your data.
The Book of R assists you to create impressive data visuals by using basic graphic tools of R. you can create amazing visuals by using different contributed packages like ggvis and ggplot2. Moreover, you can create 3D visualization by utilizing the rgl package.
This guide provides dozens of hands-on exercises to practice. You learn different statistical concepts such as exploratory data analysis and hypothesis tests. This list further includes probabilities, regression modeling, and their execution method. After the completion of this book, you will be proficient in both statistics and R’s functionalities. So the Book of R is a gateway to the world of data analysis.
This book helps you to draw the valid and useful results you achieve after data analysis. You can create publication-level graphics of your results by using different libraries of R.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
R for Data Science is very useful to transform your raw data into a convenient form to analyze data easily. So this book guides you on how to use R to make your data much more knowledgeable and understandable. This guide is a great effort to make data science fluent and fun.
R for Data Science introduces you with a collection of R packages such as RStudio and tidyverse. So these packages speed up the data analysis process. This guide is suitable for all those readers who don’t have any programming experience.
After reading thig book, you will understand the complete data science cycle along with other basic tools to manage data. Furthermore, you will find different exercises for your practices. Moreover, this book explains how to use R to solve data problems with ease and great clarity. R for Data Science helps you to analyze your data, generate hypotheses and test them quickly.
R for Excel Users: An Introduction to R for Excel Analysts
R is one of the prominent tools for statistics, machine learning, and data analysis. You can create functions, packages, and objects in this language too. Furthermore, this language is platform-independent. It means you can run this on any operating system. So the integration of R with other languages such as C and C++ is very easy. Moreover, you can create 3D visualization by utilizing the rgl package.
There are many reasons why data scientists choose R. For example, you can run R without any compiler. It is an open-source language, so it can examine the code of any application developed by R. Moreover, it is a gateway to the lucrative career. R is a cutting-edge technology that contains a robust visualization library.
R for Excel Users is all about data manipulation using R. So data manipulation includes importing and creating data sets, modifying and filtering different data sets. Moreover, it deals with summarizing and reshaping data sets. You will also explore the building blocks of R i.e. vectors and functions.
This book uses very simple and technical lingo for better understating of newcomers. Moreover, you will find different analogies to Excel where needed to clear all concepts. So this book is very useful for all type of data analysts. Here are a few incredible features of R for Excel Users: