Data Visualization in Practice

The Art and Science of Rendering Data into Insightful Visuals
1., st Auflage 2016
ISBN: 978-1-4842-1065-9
Verlag: Apress

Buch, Englisch, 400 Seiten, PB, Format (B × H): 178 mm x 254 mm

Data Visualization in Practice is a book that takes you through the theory, design, and implementation of high-impact visualizations of data.

This book couldn’t be more timely. Vast amounts of raw data are being generated by modern technologies, human life and, increasingly, from nature itself through sensors that measure every niche of the earth, from mountains to oceans, as well as the outer atmosphere and space. Powerful hardware and sophisticated algorithms are being deployed to process this data. But the velocity of growth of raw data continues to outpace the ability of today’s Big Data/analytics infrastructure to make sense of it all. And because the human brain is still the last stop for the end result of all this frenzied data wrangling, the time has come to provide just the information people need when they need it. This combination of factors means that data visualizations are mandatory to tame the data flood, and render it into meaningful insights.

That’s just what data scientist and entrepreneur Suresh Jois helps you do in Data Visualization in Practice. As he demonstrates, unlike most other branches of technology and engineering, data visualization requires a rare combination of technical and aesthetic skills, combined with an intuitive understanding of the psychology of communication. This combination of skills makes data visualization a challenging, but also a fun and adventurous skill to learn. It is the last, most crucial and mandatory step in the complex processes of data science. Jois will help you use data visualization to bring to life—in vivid and spectacular fashion—the entire investment of time, money, and human effort that goes into complex projects.

Zielgruppe

Popular/general

Autoren/Hrsg.

Jois, Suresh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung

Inhaltsverzeichnis

Mini Outline (Cover each chapter. As an alternative, you can write a one- or two-sentence description of each chapter):Chapter 1: Introduction to Data Visualization: What, Where, and Why

Chapter Goal: Provide a rapid overview of DV, by explaining the reasons and motivations for, and application contexts of DV, with several examples.
* Why data should be visualized* Iconic and typical examples of DVChapter 2: The Evolution of Data Visualization Chapter Goal: Provide the background and evolutionary stages of DV.
* The pre-history of DV* DV at the dawn of the print age* DV at the dawn of the Industrial Revolution* DV at the dawn of the computer age* DV in modern timesChapter 3: Neuropsychological Basis of Data VisualizationChapter Goal: Provide an overview of the human visual system, with explanations of how the functional principles of the visual system must be considered in all aspects of DV.
* An overview of the human visual system and its neuropsychology* What happens when DV is used without consideration of how our visual system works* How good DV incorporates knowledge of the human visual systemChapter 4: Theory of Data VisualizationChapter Goal: Provide a detailed explanation of the currently prevalent theories and frameworks of DV, with working examples of each.
* The aesthetic theory of DV* The theory of data patterns, and their corresponding DV patterns* Patterns of narrative flow, and their relevance to DVChapter 5: Software Tools for Data VisualizationChapter Goal: Provide an overview
of the currently prevalent software tools used for DV, using the comparative technique of solving the same set of DV problems with different software tools.
* Software technology stacks used for DV* Programming languages used in DV* Examples of the same DV problems implemented in multiple DV toolsChapter 6: Data Visualization Process FlowChapter Goal: Provide an overview and examples of the full process of DV, in the overall context of data analytics/data science.
* The data analytics/data science process flow* The DV process flow* Examples of DV as a capstone to several types of data analytics/data science casesChapter 7: Data Visualization Case Studies Based on Data PatternsChapter Goal: Provide in-depth case studies of DV, based on the diverse patterns of quality, quantity, and internal structure of the input data that is to be visualized.
* Overview of patterns found in raw data, or data output from analysis algorithms* Examples and case studies of DV corresponding to each data patternChapter 8: Data Visualization Case Studies Based on Application Domains and Industry VerticalsChapter Goal: Provide in-depth case studies of DV, based on the specific and unique contexts of application domains and industry verticals, like healthcare, pharma, media, textual data, finance, transportation, government, nonprofit, education, and many others.
* Overview of business processes and industry verticals that typically use DV* Case studies of DV from at least five industry verticals and business processesChapter 9: Topics and Techniques Related to Data VisualizationChapter Goal:

Provide explanations of what other topics and techniques go hand in hand with DV, such as narrative explanations of visuals, user interface design for Interactive (as against static) DV, presentation techniques needed to effectively convey a DV to its target audience, etc.
* Aesthetic and artistic aspects of DV* Narrative and textual issues that are complementary to visual aspects of DV* Interactive aspects of DV* User interface design for DV* Role of animation in DVChapter 10: The Future of Data VisualizationChapter Goal: Provide an overview of the possible future evolutionary trajectories of DV, based on emerging technologies like Google Glass, augmented reality, consumer visual reality headsets, direct retinal projectors, visual prosthetics, self-driving vehicles and many others. Cite examples from actual prototypes and concept designs, science-fiction movies, etc.
* Future sources of data* How humans will consume data in the future* Emerging display technologies* Plausible scenarios for future evolution and techniques of DV

Über Autor(innen)

Suresh Jois has worked in the software industry for several years, as a programmer, manager, consultant, and entrepreneur. He started his career in as a scientific programmer, developing code for analysis and visualization in fields ranging from astrophysics to molecular biology. He then ventured out as a consultant, developing analytics, visualization, and business process applications for health care, sports biology, and apparel design among others. He has worked on a variety of web application projects, in the fields of e-commerce, portals, personalization and recommendation engines, digital/on-line media, social networking, enterprise integration and others. Currently he is a consultant and trainer in analytics, data science, big data and visualization.
Apart from his core professional activities, Suresh is also interested in world music (he was an RJ of world music at a public radio station), fiction writing, and documentary film making. He has a keen interest in the welfare of children with neurological diseases. When possible, he contributes to non-profits in this area, as well teaching and contributing code in the field of Computational Neuroscience.