I’ve been working on the design of a new course at NYU Stern called Data Visualization.  The audience for the course are executives and MBA students.

Designing a new course requires an extensive literature survey the field. Data visualization is comprised of many disciplines including cognitive psychology, statistics, data mining, graphic design, and information design.  This course draws upon many scholars, artists, and practitioners for inspiration.  Some notable sources of inspiration include: John Maeda, Edward Tufte, Richard Mayer, Stephen Few, Dona Wong, Hans Rosling, Nathan Yau, John Tukey, Ben Fry, Casey Reas, Colin Ware, and Richard Wurman.

Through my journey to find the meaning of data visualization, I discovered that like most interdisciplinary fields there are many definitions and interpretations.  I found a few reoccurring themes in the literature in which I am designing my course around:

1) Data Visualization as Communication.  This theme focuses on communicating a message through the graphical representation of data.  Typically this involves working with historical data to tell a story or deliver a specific message. Visualizations used as communication tools are typically delivered via print/online (illustration) or through presentation (live or recorded). Powerpoint, web pages, and print media are common ways to deliver messages using a data visualization.

2) Data Visualization as Exploration.  The are a number of applications, tools, and models available that enable and promote the visual exploration of data (Big and small). This can enable those that work with the data to see patterns, trends, similarities, and differences. Visualizations used as exploratory tools are typically embedded in software packages such as SPSS, SAS, and/or invoked as part of the data analysis process as used in R or Python. Ultimately, these visualizations could be used as communication, but initially they are used as a tool in data analysis.

3) Data Visualization a Notification. In a data driven society, information drives decision making.
This involves using visualizations to communicate the performance events taking place in the present. Information dashboards, mobile apps such a Nike +,  and social media analytic tools are all examples of data visualization as notification.

These categories are not mutually exclusive. However, each category requires a unique toolkit and set of skills.

Here’s a preview of my course that is informed by these three themes.

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