Data Visualization and Text Principles and Practices: The Eye of Data Science

About

Features

• Expert coverage of all three application areas: exploratory data analysis, model diagnostics, and presentation graphics
• Follows the proven structure of previous books in our Modeling Techniques in Predictive Analytics Series: states a problem and business case, and shows managers, analysts, and programmers exactly how to solve it
• Packed with real-world examples from marketing, finance, sports analytics, web and network data science, text analytics, and social network analysis
• Teaches with R, Python, D3, Gephi, and other widely used open source programming environments

Description

Data visualization is increasingly central to predictive analytics and data science. The book focuses on all three application areas of data visualization: exploratory data analysis, model diagnostics, and presentation graphics. Built on the same structure and approach as other books in Thomas W. Miller's popular Modeling Techniques series, it has been carefully designed to serve multiple audiences: business managers, analysts, programmers, and students.

Miller begins with core principles, revealing why some data visualizations effectively present information and others don't. He reviews the science of human perception and cognition, proven principles of graphic design, and the growing role of visualization throughout data science -- including examples such as the visualization of time, networks, and maps.

Drawing on his pioneering experience teaching data visualization, Miller begins each chapter by stating a real business problem. He explains why the problem is important, describes a relevant dataset, and guides you through solving it with leading open-source tools such as R, Python, D3, and Gephi. (All R and Python code is set apart, so managers or analysts who aren't interested in programming can easily skip it.)

Like other books in this series, Data Visualization Principles and Practices draws realistic examples from key application areas, including marketing, finance, sports analytics, web and network data science, text analytics, and social network analysis. Examples include cross-sectional data, time series, network, and spatial data. Readers will discover advanced methods for constructing static and interactive graphics, building web-browser-based presentations, and even creating "information art."

Sample Content

Table of Contents

Session 1. Introducing Data Visualization
Session 2. Perception and Cognition
Session 3. Discovery (Exploratory Data Analysis)
Session 4. Visualizing Time
Session 5. Visualization Systems
Session 6. Visualizing Networks
Session 7. Maps
Session 8. Web-Browser-Based Presentations
Session 9. From Data Visualization to Information Art

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