Data Visualization: Exploring and Explaining with Data

✍ Scribed by Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

• Publisher: Cengage Learning
• Year: 2021
• Tongue: English
• Leaves: 418
• Category: Library

✦ Synopsis

DATA VISUALIZATION: Exploring and Explaining with Data is designed to introduce best practices in data visualization to undergraduate and graduate students. This is one of the first books on data visualization designed for college courses. The book contains material on effective design, choice of chart type, effective use of color, how to both explore data visually, and how to explain concepts and results visually in a compelling way with data. The book explains both the "why" of data visualization and the "how." That is, the book provides lucid explanations of the guiding principles of data visualization through the use of interesting examples.

✦ Table of Contents

Cover
Brief Contents
Contents
About the Authors
Preface
Chapter 1: Introduction
1-1: Analytics
1-2: Why Visualize Data?
1-3: Types of Data
1-4: Data Visualization in Practice
Summary
Glossary
Problems
Chapter 2: Selecting a Chart Type
2-1: Defining the Goal of Your Data Visualization
2-2: Creating and Editing Charts in Excel
2-3: Scatter Charts and Bubble Charts
2-4: Line Charts, Column Charts, and Bar Charts
2-5: Maps
2-6: When to Use Tables
2-7: Other Specialized Charts
2-8: A Summary Guide to Chart Selection
Summary
Glossary
Problems
Chapter 3: Data Visualization and Design
3-1: Preattentive Attributes
3-2: Gestalt Principles
3-3: Data-Ink Ratio
3-4: Other Data Visualization Design Issues
3-5: Common Mistakes in Data Visualization Design
Summary
Glossary
Problems
Chapter 4: Purposeful Use of Color
4-1: Color and Perception
4-2: Color Schemes and Types of Data
4-3: Custom Color Using the Hsl Color System
4-4: Common Mistakes in the Use of Color in Data Visualization
Summary
Glossary
Problems
Chapter 5: Visualizing Variability
5-1: Creating Distributions from Data
5-2: Statistical Analysis of Distributions of Quantitative Variables
5-3: Uncertainty in Sample Statistics
5-4: Uncertainty in Predictive Models
Summary
Glossary
Problems
Chapter 6: Exploring Data Visually
6-1: Introduction to Exploratory Data Analysis
6-2: Analyzing Variables One at a Time
6-3: Relationships between Variables
6-4: Analysis of Missing Data
6-5: Visualizing Time Series Data
6-6: Visualizing Geospatial Data
Summary
Glossary
Problems
Chapter 7: Explaining Visually to Influence with Data
7-1: Know Your Audience
7-2: Know Your Message
7-3: Storytelling with Charts
7-4: Bringing It All Together: Storytelling and Presentation Design
Summary
Glossary
Problems
Chapter 8: Data Dashboards
8-1: What Is a Data Dashboard?
8-2: Data Dashboards Taxonomies
8-3: Data Dashboard Design
8-4: Using Excel Tools to Build a Data Dashboard
8-5: Common Mistakes in Data Dashboard Design
Summary
Glossary
Problems
Chapter 9: Telling the Truth with Data Visualization
9-1: Missing Data and Data Errors
9-2: Biased Data
9-3: Adjusting for Inflation
9-4: Deceptive Design
Summary
Glossary
Problems
References
Index

✦ Subjects

WCN: 02-300