ICS 484: Data Visualization

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:

1. Know- Breadth and Depth of Knowledge

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.

1. Do - Intellectual and Practical Skills

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.

1. Value - Personal and Social Responsibility

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

• a. Students can apply knowledge of computing and mathematics appropriate to the discipline
• b. Students can analyze a problem, and identify and define the computing requirements appropriate to its solution
• c. Students can design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs
• d. Students can function effectively on teams to accomplish a common goal
• e. Students have an understanding of professional, ethical, legal, security and social issues and responsibilities
• f. Students can communicate effectively with a range of audiences
• g. Students can analyze the local and global impact of computing on individuals, organizations, and society
• h. Students can recognize the need for and an ability to engage in continuing professional development
• i. Students can use current techniques, skills, and tools necessary for computing practice
• j. An ability to use and apply current technical concepts and practices in the core information technologies. [BA IT only]
• l. An ability to effectively integrate IT-based solutions into the user environment. [BA IT only]
• m. An understanding of best practices and standards and their application. [BA IT only]
• n. An ability to assist in the creation of an effective project plan. [BA IT only]

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

Schedule

• Week 1: Introduction to Data Visualization, History of Visualization, Examples of Visualizations
• Week 2: Introduction to tools for interactive data visualization on the Web such as Javascript, D3.js, Kinetic.js, Xmdv
• Week 3: Fundamentals for creating effective tables and charts.
• Week 4: Introduction to Information Visualization & Human Perception with Examples
• Week 5: Project 1 presentations
• Week 6: Project 1 presentations
• Week 7: Introduction to Geospatial Visualization
• Week 8: Introduction to Privacy Issues in Data Visualization
• Week 9: Introduction to Social Network Visualization, Project 2 presentations
• Week 10: Project 2 presentations
• Week 11: SPRING BREAK
• Week 12: Introduction to Scientific and Medical Visualization
• Week 13: Introduction to Dynamic Data Visualization
• Week 14: Visual Analytics
• Week 15: Introduction to Big Data Visualization!
• Week 16: Project 3 presentations