Human-Centered Science Design

Emphasis Area Overview

The Human Centered Science Design emphasis develops an understanding of theoretical foundations and the necessary hands-on experience to understand the strengths and limitations of different methods.

Students learn the significance of each component in the information lifecycle and its impact on technical and social data analytics.


Covers the fundamental concepts of current visualization concepts and technologies, adding in Infographic and Interactive Visualization Design. Unlike many data visualization courses, this one focuses on principles of visualization design and the grammar of graphics as they can be applied to combining art and technology to tell data stories. These principles are then implemented in popular contemporary visualization technologies. Students will develop an advanced knowledge of the appropriate selection, modeling, and evaluation of data visualizations.

3 Credit Hours

Usability is concerned with how well a person can use a designed system to accomplish the goals for which that system is designed. This course provides an overview of methods for usability testing of data science applications through readings, examples and discussions. Students will work in groups to develop and present a usability test plan for a data science application or system.

3 Credit Hours


Graduates of the Masters of Science in Data Science and Analytics who pursue the Human Centered Science Design emphasis area will achieve the following educational objectives:

  • Students will develop a deep understanding of the theoretical foundations and hands-on experience necessary to understand the strengths and limitations of different analytical methods.
  • Combines both the technical (databases, social networking, data mining, and text mining) and social (economic, ethical, policy, and political) aspects of data analytics.
  • Students will build an understanding of the complex interplay between the decisions made during the collection, curation, and transformation steps in the information lifecycle, and their impact on the analytical methods that should be employed.