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Data Visualization


Data Visualization
Robert M. Haralick
Course Rationale
Today quantitative and symbolic data are easily collected in computer format, from databases, websites, smartdevices, and anything that has interconnect capabilities. When such large amounts of data are put in spreadsheets or tabular reports, it becomes difficult to see the patterns, structure, trends, or relationships inherent in the data. Effective data visualization exposes these inherent relationships, consolidating and illustrating them in graphics.
Course Description
A visualization organizes data in a way that the structure and relationships in the data that may not be so easily understood becomes easily understood and interpreted with the visualization. Visualizations of a data set give the reader a narrative that tells the story of the data. The purpose of data visualization is to convey information contained in data to clearly and efficiently communicate an accurate picture of what the data says through understandable and context appropriate visualizations. To do a visualization can be just exploratory or entails using Machine Learning techniques that determine the structure of the data. The visualizations are then matched to the data structure.

Schedule of Lecturers
The course will explore how principles of information graphics and design and how principles of visual perception, can be used with machine learning techniques to make effective data visualizations. Some of the lectures will be given by invited speakers. The first invited lecturer will be Dona Wong who for many years lead the visualization work produced by the Wall Street Journal. Other invited speakers include Amanda Cox, who is the Graphics Editor at the New York Times, Enrico Bertini from NYU, and a principal organizer of the BELIV workshops on Visualization Evaluations, Kristen Sosulski from NYU, Sharon Hsiao, from Arizona State University, Kaiser Fung, Vice President of Business Intelligence and Analytics at Vimeo, a high-quality video hosting platform for creative people, Michael Grossberg from CCNY, and our own Lev Manovich, one of the most influential researchers in cultural computing and transmedia.
Each student will make a presentation of some principles of data visualizations or do a visualization project. The course is open to PhD students in all programs. Non-computer science
students will be paired with computer science students for the visualization project.
Learning Goals
• Be able to describe the key design guidelines and techniques used for the visual display of information
• Understand how to best use the capabilities of visual perception in a graphic display
• Understand the principles of interactive visualizations 
• Understand how Machine Learning techniques can determine data structure and pattern
• Explore and critically evaluate a wide range of visualization techniques and applications

Required Text:
Wong, Dona, (2013 )Wall Street Journal Guide to Information Graphics, Norton, W. W. And Company, Inc.
(Please bring this text to the first class meeting)
Text Resources
Few, Stephen, (2004) Show Me the Numbers: Designing Tables and Graphs to Enlighten, Cheshire, CT: Graphics Press.
Fung, Kaiser, (2013) Numbersense: How To Use Big Data To Your Advantage, McGraw-Hill.
Meirelles, Isabel, (2013) Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations , Rockport Publishers.
Schneiderman, Ben, (2009) Designing the User Interface: Strategies for Effective Human-Computer Interaction (5th Edition), Prentice Hall. Tufte, E. R. (1990)., Envisioning Information, Cheshire, CT: Graphics Press.
Tufte, E.R. (2001), The Visual Display of Quantitative Information, Graphics Pr; 2nd edition.
Wong, Dona, (2013 )Wall Street Journal Guide to Information Graphics, Norton, W. W. And Company, Inc.
Scholarly archival papers.