There are a number of terms used in Data Visualization (see this vocabulary list for more details). Many people use these terms in different ways (and there is definitely debate around some, such as infographics), but here are a few suggested definitions to help clarify the discussion in this guide:
Data Visualization is a broad term that basically involves anything that uses graphical or pictorial representations of data for exploration, sense-making, and communication. Scientific Visualization and Information Visualization are often considered subsets of Data Visualization.
Scientific Visualization normally involves visualizing scientific data that has ties to physical objects, phenomena, and processes in the real world, such as modeling a heart pumping blood or a tornado. Scientific Visualization can be highly complex and specific to discipline.
Information Visualization normally involves visualizing abstract concepts and ideas, such as creating a treemap of population by world region or a network diagram showing relationships of twitter users. Others, such as Card, Mackinlay, and Shneiderman (1999) further add that information visualization is “the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” (p. 7).
Infographics could be considered a special type of information visualization, where according to Steele and Iliinsky (2011) are “manually drawn, specific to the data at hand (and therefore nontrivial to recreate with different data), aesthetically rich, and relatively data-poor (because each piece of information must be manually encoded)” (p. 5).
Visual Analytics, a related term, defined by Keim et al (2008), “combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets” (p. 157). Roughly, the practice of analyzing data through the use of visualizations.
For more perspectives and details, check out these references below and the Books, Blogs and More page.
Bederson, B., & Shneiderman, B. (2003). The craft of information visualization: readings and reflections. Amsterdam ; Boston: Morgan Kaufmann.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: using vision to think. San Francisco, Calif: Morgan Kaufmann Publishers.
Few, S. (2009). Now you see it: simple visualization techniques for quantitative analysis. Oakland, Calif: Analytics Press.
Fry, B. (2008). Visualizing data. Sebastopol, CA: O’Reilly Media, Inc.
Iliinsky, N., & Steele, J. (2011). Designing data visualizations: Iintentional communication from data to display. Beijing: O’Reilly.
Keim, D., Andrienko, G., Fekete, J.-D., Gorg, C., Kohlhammer, J., & Melancon, G. (2008). Visual Analytics: Definition, Process, and Challenges. In A. Kerren, J. T. Stasko, J.-D. Fekete, & C. North (Eds.), Information Visualization: Human-Centered Issues and Perspectives (pp. 154–175). Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg.
Image excerpt from http://selection.datavisualization.ch/