# An introduction to data science

##### By: Saltz, Jeffrey S.

##### Contributor(s): Stanton, Jeffrey M [Co author].

Material type: BookPublisher: 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#descriptionItem type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library General Stacks | 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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