# 9 Data Science

## 9.1 R for Data Science

Hadley Wickham Garret Grolemund

This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.

## 9.2 R for Data Science Solutions

Solutions for the hadley and Grolemund R4Ds book

https://jrnold.github.io/r4ds-exercise-solutions/

*Yet another ‘R for Data Science’ study guide*

An alternative set of solutions for R4Ds.

## 9.3 Everyday Data Science

Andrew Carr

Everyday data science is a collection of tools and techniques you can use to master data science in your day-to-day life. There are case studies, tutorials, code snippets, pictures, math, and jokes. All designed as a fun introduction to the world of data science. Some example chapters include, A/B testing to make perfect lemonade, word vectors to improve your resume, differential equations for weight loss, and how a man used statistics to qualify for the Olympics. Life is full of decisions. We, as people, have the remarkable ability to make decisions in the face of uncertainty. We, as humans, have only recently developed the ability to use computers to process vast amounts of data to improve our decision making. This innovation has led to the development of the field of Data Science. This book is written to give tools and inspiration to aspiring decision makers. You make decisions daily and the methodology of data science can help.

Paid ~$8 https://gumroad.com/l/everydaydata

## 9.4 An Introduction to Data Analysis

Michael Franke

his book provides basic reading material for an introduction to data analysis. It uses R to handle, plot and analyze data. After covering the use of R for data wrangling and plotting, the book introduces key concepts of data analysis from a Bayesian and a frequentist tradition. This text is intended for use as a first introduction to statistics for an audience with some affinity towards programming, but no prior exposition to R.

https://michael-franke.github.io/intro-data-analysis/index.html

## 9.5 Introduction to Data Science

Rafael A Irizarry

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown.

https://rafalab.github.io/dsbook/

Pay what you want for PDF, minimum $0.00

## 9.6 Data Science: A First Introduction

Tiffany-Anne Timbers Trevor Campbell Melissa Lee

This is an open source textbook aimed at introducing undergraduate students to data science. It was originally written for the University of British Columbia’s DSCI 100 - Introduction to Data Science course. In this book, we define data science as the study and development of reproducible, auditable processes to obtain value (i.e., insight) from data.

## 9.7 Data Science at the Command Line, 2e

Jeroen Janssens

This book is about doing data science at the command line. Our aim is to make you a more efficient and productive data scientist by teaching you how to leverage the power of the command line.

## 9.8 Practical Data Science with R, Second Edition

Nina Zumel and John Mount

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

https://www.manning.com/books/practical-data-science-with-r-second-edition#toc

## 9.9 R Programming for Data Science

Roger Peng

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

## 9.10 Exploratory Data Analysis… by Roger D. Peng

Roger Peng

This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization

Pay what you want, minimum $0.00

## 9.11 edav.info/

Zach Bogart, Joyce Robbins

With this resource, we try to give you a curated collection of tools and references that will make it easier to learn how to work with data in R.

In addition, we include sections on basic chart types/tools so you can learn by doing.

There are also several walkthroughs where we work with data and discuss problems as well as some tips/tricks that will help you.

## 9.12 APS 135: Introduction to Exploratory Data Analysis with R

Dylan Z. Childs

This is the online course book for the Introduction to Exploratory Data Analysis with R component of APS 135, a module taught by the Department and Animal and Plant Sciences at the University of Sheffield. You will be introduced to the R ecosystem.You will learn how to use R to carry out data manipulation and visualisation.This book provides a foundation for learning statistics later on.

## 9.13 The Art of Data Science

Roger D. Peng and Elizabeth Matsui

A Guide for Anyone Who Works with Data

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. Printed copies are available through Lulu.

Pay what you want for the ebook, minimum $0.00

## 9.14 The Elements of Data Analytic Style

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials.

Pay what you want for the ebook, minimum $0.00

## 9.15 Beginning Data Science in R

Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. Those with some data science or analytics background, but not necessarily experience with the R programming language

Paid, ~$40

## 9.16 Business Intelligence with R

A desktop reference for busy professionals, giving you fingertip access to a variety of BI analytic methods done in R as simply as possible.

All proceeds will support mitochondrial disorder research at Seattle Children’s Hospital.

Free or up to $20 for a good cause!

## 9.17 R Data Science Quick Reference

In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.

Paid, ~$30

## 9.18 Modern Data Science with R

Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton

This book is intended for readers who want to develop the appropriate skills to tackle complex data science projects and “think with data” (as coined by Diane Lambert of Google). The desire to solve problems using data is at the heart of our approach.

We acknowledge that it is impossible to cover all these topics in any level of detail within a single book: Many of the chapters could productively form the basis for a course or series of courses. Instead, our goal is to lay a foundation for analysis of real-world data and to ensure that analysts see the power of statistics and data analysis. After reading this book, readers will have greatly expanded their skill set for working with these data, and should have a newfound confidence about their ability to learn new technologies on-the-fly.

This book was originally conceived to support a one-semester, 13-week undergraduate course in data science. We have found that the book will be useful for more advanced students in related disciplines, or analysts who want to bolster their data science skills. At the same time, Part I of the book is accessible to a general audience with no programming or statistics experience.

## 9.19 Modern Statistics with R

This book covers the fundamentals of data science and statistics. The first half deals with the basics of R and R coding, data wrangling, exploratory data analysis and more advandced programming. The second half deals with modern statistics (favouring permutation tests, the bootstrap and Bayesian methods over traditional asymptotic methods), regression models and predictive modelling. It also contains information about debugging and explanations of 25 commonly encountered error messages in R. In addition, there are 170 or so exercises with fully worked solutions.

## 9.20 Model-Based Clustering and Classification for Data Science

Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, and Adrian E. Raftery

Among the broad field of statistical and machine learning, model-based techniques for clustering and classification have a central position for anyone interested in exploiting those data. This text book focuses on the recent developments in model-based clustering and classification while providing a comprehensive introduction to the field. It is aimed at advanced undergraduates, graduates or first year PhD students in data science, as well as researchers and practitioners.