An introduction on how graphical representations of data can be used to aid understanding. This course details the theory and practice of designing effective information or scientific visualizations. The techniques learned in this class have wide applications to all fields in engineering and science, where due to increasing sizes and complexity data now demands effective presentation and analysis. Topics will include isosurfaces, volume rendering, transfer functions, vector/tensor fields, topological analysis, large data visualization, and uncertainty in visualizations.

Upon completion of the course, successful students will be able to:

• understand the current state-of-the-art in visualization technologies;
• understand the underlying perceptual theory, mathematics, algorithms, and data structures that drive visualizations
• create interactive 2D/3D information and scientific visualization programs

• CMPS 1600 or good programming skills (python and javascript will be used in the course)

Brian Summa

Cody Licorish

Summa: Monday, 1:00PM-3:00PM, Wednesday, 2:00PM-3:00PM, Book a time
Licorish: Tuesday, 1:00PM-2:00PM, Wednesday, 1:00PM-2:00PM, 309 STH

Monday, Wednesday and Friday, 3:00PM - 3:50PM, ST 302

Required:

• Visualization Analysis and Design, Tamara Munzner, CRC Press (2014)

Recommended Resources:

• Interactive Data Visualization for the Web, Scott Murray, O�Reilly (2013)
• VTK User's Guide, Kitware, Kitware (2013)

Undergrads:
10% Participation
40% Assignments
20% Midterm Exam
30% Final Exam

Grads:
10% Participation
30% Assignments
15% Midterm Exam
15% Final Exam
30% Final Project

Assignment Late Policy: 20% reduction within 1 week of due date (not applicable to projects)

Final grade will be assigned as follows:
A >= 93% [Exemplary. Superior achievement.]
A- >= 90% [Outstanding]
B+ >= 87%, B >= 83%, B- >= 80% [High Pass, Above Average]
C+ >= 77%, C >= 73%, [Pass, Average work, Satisfactory] C- >= 70% [Below Average]
D >= 60%, [Low Pass, Unsatisfactory, Barely passing]
F < 60% [Failure, Unacceptable]

Grad students will complete a visualization project using the concepts outlined in this course. Projects must be pitched and approved by the instructor by 5th week of the semester. Projects must be significant in scope to receive approval. Projects can come in one of two flavors: (1) Find and implement a technique from an IEEE SciVis or InfoVis paper from the past 5 years (2) Design and develop an interactive visualization tool for data in your field of expertise.

We will use Canvas to store lecture files and as the record for grades.

All communication for this course will be through slack: http://tulane-vis-2019.slack.com. Slack is not FERPA compliant, so email instructors directly if the subject is personal/sensitive. A link for an automatic invite to the class slack in on the canvas homepage.

Starter code and assignment submission will be through Github Classroom. Links are provided in the course schedule.

You are required to adhere to the Code of Academic Conduct. Cheating will be reported to the Associate Dean of Newcomb-Tulane College. I encourage collaboration, but everyone's work must be their own. Help and sharing of small code snippets to help someone get past a bug are OK, but whole files or classes are not. Sources other than the textbook should be cited appropriately.

Tulane University recognizes the inherent dignity of all individuals and promotes respect for all people. As One Wave, Tulane is committed to providing an environment free of all forms of discrimination and sexual harassment, including sexual assault, domestic and dating violence, and stalking. If you (or someone you know) has experienced or experiences gender-based violence, know that you are not alone. Learn more at onewave.tulane.edu.

Schedule is subject to change.