Klaus Mueller

This course is an introduction to both the foundations and applications of visualization and visual analytics, for the purpose of understanding complex data in science, medicine, business, finance, and many others. It will begin with the basics - visual perception, cognition, human-computer interaction, the sense-making process, data mining, computer graphics, and information visualization. It will then move to discuss how these elementary techniques are coupled into an effective visual analytics pipeline that allows humans to interactively think with data and gain insight. Students will get hands-on experience via several programming projects, using popular public-domain statistics and visualization libraries and APIs. This course is offered as both CSE 332 and ISE 332.

• An ability to transform numerical datasets from science and medicine into understandable visual representations.
• An understanding of the issues associated with digital image quality (e.g. sampling artifacts) and algorithms for performing basic image manipulation operations such as filtering, re-sampling, and intensity transformation.
• Working knowledge of methods (including graphical user interfaces) for the visualization of three-dimensional data sets.

• Computer Graphics: Principles and Practice 2 edition in C, J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes, Addison-Wesley, 1995
• Introduction to Volume Rendering Lichtenbelt, R. Crane, S. Naqvi Prentice-Hall, 1998

• Applications of visual data science, visual analytics, and basic tasks
• Visual perception and cognition
• Visual design and aesthetics
• Human-computer interaction and graphical user interface design
• Tools – python for data analysis, D3.js for visualization
• The human sense-making process
• Techniques in data mining – cluster and outlier analysis, text and pattern mining. classifiers
• Computer graphics  - color, shading, illumination, lighting models, volume rendering
• Scientific and medical visualization – techniques to visualize spatial (3D) data
• Information visualization – techniques to visualize non-spatial data
• High-dimensional data, dimensionality reduction, the curse of dimensionality 
• Streaming  and time-varying data
• Very large and  massive (“big”) data – data reduction, summarization, management
• Qualitative and quantitative evaluation – user studies, statistical evaluation
• Collaborative visualization
• Use cases and application of visualization, visual analytics, and visual data science 

• Visual cluster analysis – use of python for data analysis and D3.js for visualization
• Analysis and volume rendering of medical data – use an existing renderer to understand the image generation process
• Visual text mining – use of python for data analysis and D3.js for visualization
• Visual analysis of large graphs -- use of python for data analysis and D3.js for visualization
• Visual analysis of streaming, time-varying data -- use of python for data analysis and D3.js for visualization

Crosslisted with CSE332