Introduction to Data Science

Data Analysis and Prediction Algorithms with R

by Rafael A Irizarry

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Book Description

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. The book is divided into six parts: R, Data Visualization, Data Wrangling, Probability, Inference and Regression with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture. The book includes dozens of exercises distributed across most chapters.

This open book is licensed under a Creative Commons License (CC BY-NC-SA). You can download Introduction to Data Science ebook for free in PDF format (55.8 MB).

Table of Contents

Part I
R
 
Chapter 1
Getting Started with R and RStudio
 
Chapter 2
R Basics
 
Chapter 3
Programming basics
 
Chapter 4
The tidyverse
 
Chapter 5
Importing data
 
Part II
Data Visualization
 
Chapter 6
Introduction to data visualization
 
Chapter 7
ggplot2
 
Chapter 8
Visualizing data distributions
 
Chapter 9
Data visualization in practice
 
Chapter 10
Data visualization principles
 
Chapter 11
Robust summaries
 
Part III
Statistics with R
 
Chapter 12
Introduction to Statistics with R
 
Chapter 13
Probability
 
Chapter 14
Random variables
 
Chapter 15
Statistical Inference
 
Chapter 16
Statistical models
 
Chapter 17
Regression
 
Chapter 18
Linear Models
 
Chapter 19
Association is not causation
 
Part IV
Data Wrangling
 
Chapter 20
Introduction to Data Wrangling
 
Chapter 21
Reshaping data
 
Chapter 22
Joining tables
 
Chapter 23
Web Scraping
 
Chapter 24
String Processing
 
Chapter 25
Parsing Dates and Times
 
Chapter 26
Text mining
 
Part V
Machine Learning
 
Chapter 27
Introduction to Machine Learning
 
Chapter 28
Smoothing
 
Chapter 29
Cross validation
 
Chapter 30
The caret package
 
Chapter 31
Examples of algorithms
 
Chapter 32
Machine learning in practice
 
Chapter 33
Large datasets
 
Chapter 34
Clustering
 
Part VI
Productivity tools
 
Chapter 35
Introduction to productivity tools
 
Chapter 36
Organizing with Unix
 
Chapter 37
Git and GitHub
 
Chapter 38
Reproducible projects with RStudio and R markdown
 

Book Details

Publisher
Leanpub
Published
2019
Pages
722
Edition
1
Language
English
PDF Size
55.8 MB
License
CC BY-NC-SA

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