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An introduction to data science

By: Saltz, Jeffrey S.
Contributor(s): Stanton, Jeffrey M [Co author].
Material type: materialTypeLabelBookPublisher: Los Angeles Sage Publications 2018Description: xii, 275 p. Includes index.ISBN: 9781506377537.Subject(s): Data | Databases | R - Computer program language | Statistics - InterpretationDDC classification: 005.74 Summary: 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. https://www.sagepub.com/hi/nam/an-introduction-to-data-science/book256486#description
List(s) this item appears in: Data Science
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Slot 84 (0 Floor, West Wing) Non-fiction 005.74 S2I6 (Browse shelf) Available 199287

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

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.

https://www.sagepub.com/hi/nam/an-introduction-to-data-science/book256486#description

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