An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio® from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions.   The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstrates how to use data effectively and efficiently to construct models, predict outcomes, visualize data, and make decisions. Accompanying digital resources provide code and datasets for instructors and learners to perform a wide range of data science tasks.  

Table of Contents:
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