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Department: Data Analysis and Visualization

Director: Professor Matthew Gold

The Graduate Center

365 Fifth Avenue

New York, NY 10016

Email: datavis@gc.cuny.edu

https://www.gc.cuny.edu/datavis

FACULTY

Katherine Behar, Cathy Davidson, Scott Dexter, Howard Everson, Kevin Ferguson, Eleanor Frymire, Matthew Gold, Michael Mandiberg, Lev Manovich, Michelle McSweeney, Jeremy Porter, Lisa Rhody, Aucher Serr, Timothy Shortell

THE PROGRAM

The Master of Science in Data Analysis and Visualization offers three areas of study: Data Analysis, Data Visualization, and Data Studies.

In Data Analysis, students will begin with basics of working with data—“cleaning” data, preparing it for analysis, and working with a variety of data formats. They next learn fundamental concepts and methods for data exploration and statistics. After that, students learn and practice contemporary methods for data analysis including machine learning and AI. In these classes, we focus on analyzing real-world data sets. Students also learn techniques for working with very big data. The classes use two of the most popular programming languages for data analysis today: R and Python.

In Data Visualization, courses are designed to teach basic and advanced visualization methods appropriate for visualizing quantitative, network, text, visual, spatial, and temporal data. Students will learn how to create static, animated, and interactive visualizations, data-centric publications, and maps. They will also learn principles of graphic and user interaction design and visual communication necessary for the creation of effective and engaging visualizations.

In Data Studies, students will consider data through the lenses of media theory and history, software studies, and cultural theory. These courses will help students to think critically and historically about contemporary methods, techniques, and software for working with data. These courses will be useful for students who plan to pursue doctoral programs in design, communication, humanities, or social sciences, and they will help students employ methods used in a variety of employment areas. Students will also understand longer historical trends that drive the adoption of computers, networks, and data analysis in a society, and this will help them to anticipate future trends.