Areas of Study
We offer three areas of study: Data Analysis, Data Visualization, and Data Studies. Across all of these classes, we move from fundamental concepts and methods to more advanced methods. We help students gain practical skills in working with data and the theoretical skills to anticipate the future use of data in society, and to understand the possibilities, implications, and limitations of data. Graduates will be able to work in the industry (data analysis, data and information visualization) or to pursue doctoral studies in a range of related disciplines.
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 datasets. 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.