An introduction to data science. (2017)

• Record Type: Book
• Further Information: Note: Jeffrey S. Saltz, Jeffrey M. Stanton.
• Authors: Saltz, Jeffrey S; Stanton, Jeffrey M, 1961-
• 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;
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 005.74; Databases; Data mining; Information visualization; R (Computer program language)
• Languages: English
• ISBNs: 9781506377513
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.