ISE 5760
Transcript Abbreviation:
Vis & HCI
Course Description:
Students learn about information visualization techniques that help people analyze massive amounts of digital data to combat overload and aid sensemaking with applications in retail and financial decision making, logistics, information systems, manufacturing, healthcare, energy and smart grids, cybersecurity and social networks.
Course Levels:
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
9.00
Total Completions Allowed:
3.00
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Jr, Sr, or Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 773.01.
Course Goals / Objectives:
Students will learn the key concepts in visual analytics and how to use them to combat
data overload.
data overload.
Students will learn the cognitive components of analytical process.
Students will learn the basic techniques for using visualizations to aid sensemaking and
other cognitive components in analytical process.
other cognitive components in analytical process.
Students will know basic vulnerabilities that can lead to shallow, narrow, erroneous
analyses and will be able to critique an analysis based on the definition of what is
sufficient rigor.
analyses and will be able to critique an analysis based on the definition of what is
sufficient rigor.
Students will be know how to communicate analytical results including uncertainty and
risk visually to policy or decision makers.
risk visually to policy or decision makers.
Students will be able to test whether new computer and visualization tools support good
analytic process and reduce the vulnerability to shallow/narrow analysis.
analytic process and reduce the vulnerability to shallow/narrow analysis.
Students will design visualizations that reveal patterns in large data bases.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| What is data overload and why is it a persistent problem How do people avoid overload when perceiving natural scenes Cases of computer displays making overload worse | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Representation aiding Representation effect Aiding performance through visualization | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Digital media and visualization Digital media and symbols Analog vs. digital representations | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Basic techniques for representation aiding Visualizing relationships Frames of reference Data in context Highlighting events and contrasts | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Integrated and Pattern-based Visualizations Robot handler; mission problem holder Human robot ratio Case: Rescue robots | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Navigating Computer Displays Keyhole effect, Visual momentum Longshot displays Multiple perspectives | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Integration Review of fundamental principles Escaping data overload | 4.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 28 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 20% |
| Quizzes | 20% |
| Design exercises | 60% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Display and Interface Design: Subtle Science, Exact Art | Bennett, Flach | |
| Course readings | Various |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: