An Introduction to Data Science

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 (R) 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 (R) 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