Introduction to Data Science (eBook)

Preface
About the Authors
Introduction: Data Science, Many Skills
What Is Data Science?
The Steps in Doing Data Science
The Skills Needed to Do Data Science
Chapter 1 • About Data
Storing Data—Using Bits and Bytes
Combining Bytes Into Larger Structures
Creating a Data Set in R
Chapter 2 • Identifying Data Problems
Talking to Subject Matter Experts
Looking for the Exception
Exploring Risk and Uncertainty
Chapter 3 • Getting Started With R
Installing R
Using R
Creating and Using Vectors
Chapter 4 • Follow the Data
Understand Existing Data Sources
Exploring Data Models
Chapter 5 • Rows and Columns
Creating Dataframes
Exploring Dataframes
Accessing Columns in a Dataframe
Chapter 6 • Data Munging
Reading a CSV Text File
Removing Rows and Columns
Renaming Rows and Columns
Cleaning Up the Elements
Sorting Dataframes
Chapter 7 • Onward With RStudio®
Using an Integrated Development Environment
Installing RStudio
Creating R Scripts
Chapter 8 • What’s My Function?
Why Create and Use Functions?
Creating Functions in R
Testing Functions
Installing a Package to Access a Function
Chapter 9 • Beer, Farms, and Peas and the Use of Statistics
Historical Perspective
Sampling a Population
Understanding Descriptive Statistics
Using Descriptive Statistics
Using Histograms to Understand a Distribution
Normal Distributions
Chapter 10 • Sample in a Jar
Sampling in R
Repeating Our Sampling
Law of Large Numbers and the Central Limit Theorem
Comparing Two Samples
Chapter 11 • Storage Wars
Importing Data Using RStudio
Accessing Excel Data
Accessing a Database
Comparing SQL and R for Accessing a Data Set
Accessing JSON Data
Chapter 12 • Pictures Versus Numbers
A Visualization Overview
Basic Plots in R
Using ggplot2
More Advanced ggplot2 Visualizations
Chapter 13 • Map Mashup
Creating Map Visualizations With ggplot2
Showing Points on a Map
A Map Visualization Example
Chapter 14 • Word Perfect
Reading in Text Files
Using the Text Mining Package
Creating Word Clouds
Chapter 15 • Happy Words?
Sentiment Analysis
Other Uses of Text Mining
Chapter 16 • Lining Up Our Models
What Is a Model?
Linear Modeling
An Example—Car Maintenance
Chapter 17 • Hi Ho, Hi Ho—Data Mining We Go
Data Mining Overview
Association Rules Data
Association Rules Mining
Exploring How the Association Rules Algorithm Works
Chapter 18 • What’s Your Vector, Victor?
Supervised and Unsupervised Learning
Supervised Learning via Support Vector Machines
Support Vector Machines in R
Chapter 19 • Shiny® Web Apps
Creating Web Applications in R
Deploying the Application
Chapter 20 • Big Data? Big Deal!
What Is Big Data?
The Tools for Big Data
Index