Description: Introduction to data visualization through practical techniques for turning data into images to produce insight. Topics include: information visualization, geospatial visualization, scientific visualization, social network visualization, and medical visualization.
Objectives: Program Learning Objective: The objective is to introduce students about the broad field of data visualization, and relevant software tools, so that they are able to obtain basic mastery of the skills needed to: 1. turn raw data into effective visualizations of the data, 2. evaluate the efficacy of visualizations.
Institutional Learning Objective:
Students will develop their understanding of the world with emphasis on Hawai’i by the use of geospatial data sets in the class, producing visualizations and therefore potentially hidden insights into regions of the world in relation to each other. Data may be about the economy, energy use, climate, for example.
Students improve their abilities to think critically and creatively through designing, questioning and critiquing the visualizations produced by experts as well as their peers. Students conduct research by examining visualizations approaches used frequently online and described in conference and journal articles. Students improve their communication and reporting skills by having to give an oral presentation and demonstration of their visualization.
Students demonstrate excellence, integrity, and engagement through team-based collaborative projects that require them to learn how to work with differences in cultures and personal identities. Students will gain a better understanding of issues relating to the stewardship of he natural environment and civic participation in their communities through the data sets they will attempt to interpret and visualize.!
Course Learning Outcomes: 1. Students develop software programs for producing data visualizations. 2. Students learn about the nuances in the different types of data visualizations- including information visualization, geospatial visualization, scientific visualization, social network visualization, medical visualization, 3. Students can evaluate data visualization approaches critically. 4. Students can present and explain their data visualizations. 5. Students can learn to work in teams to co-develop data visualizations.
Program Learning Outcomes
Prerequisites: two ICS 300-level courses.
Textbook(s): Matthew O. Ward, Interactive Data Visualization: Foundations, Techniques, and Applications, A K Peters/CRC Press.
Selected Papers from IEEE Visualization Conference Proceedings
Grading: Project 1, 2, 3 - each 30% of grade Class participation: 10% of grade