Human- Centered Design for Data
Human-Centered Design for Data Overview
The Human Centered Design for Data 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.
Courses
DATA_SCI 8654 Advanced Visualization and Communication I
Synopsis: 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 Hour
3 – Credit Hour
DATA_SCI 8656 Advanced Visualization and Communication II
Synopsis: 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 & 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 Hour
3 – Credit Hour
Outcomes
Graduates of the Masters of Science in Data Science and Analytics who pursue the Human Centered Data 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.
Faculty
Sean Goggins
Director, Data Science & Analytics MS Program, Course Coodinator
113 Naka Hall
(215) 948-2729
gogginss@missouri.edu
Sample Course Path
Students move through 8 week modules completeting core courses and then progressing through emphasis area concentration courses directly applicable with their area of study.
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