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Information and Scientific Visualization
CMPS-3360/6360, Spring 2017

Course Information
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.
Learning Outcomes
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 (C and C++ and javascript will be used in the course)
Instructor Office Hours
Monday, Wednesday and Friday, 10:00AM - 10:50AM, ST 302
  • Visualization Analysis and Design, Tamara Munzner, CRC Press (2014)
Recommended Resources:
  • Interactive Data Visualization for the Web, Scott Murray, O‚ÄôReilly (2013) Free Online
  • VTK User's Guide, Kitware, Kitware (2013)
10% Participation
15% Quizes
45% Assignments
30% Final Exam

10% Participation
15% Quizes
15% Paper Reading
30% Assignments
30% Final Project

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

Final Project
Grad students (required) and advanced undergrads (in lieu of a final exam) will have the option to complete a visualization project using the concepts outlined in this course. Projects must be pitched and approved by the instructor by 6th week of the semester. Projects must be significant in scope to receive approval. On example of a final project is to implement a technique from an IEEE SciVis or InfoVis paper from the past 5 years.
Collaboration and Academic Integrity
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.
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Timing and topics may be adjusted.

Week Topic
1 Introduction
2 Graphics Background
3 Perception, Cognition, Color
4 Marks and Channels
5 Views, Focus, and Interaction
6 Tabular Data, Trees, and Graphs
7 Maps and Geospatial
8 Visualizing Text and Sets
9 Grids and Isosurfaces
10 Volume Rendering and Transfer Functions
11 Advanced Volume Rendering and Transfer Functions
12 Flow Visualization, Vectors, and Tensors
13 Topological Features
14 Large Data Visualization
15 Dealing with Uncertainty