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.

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• An ability to transform spatial and non-spatial data from science, medicine, commerce, etc. into interactive visual representations.
• An understanding of the perceptual and cognitive reasoning processes that occur in humans when exploring visual artifacts derived from data to gain insight into the underlying phenomena.
• Working knowledge of principles and methods in human-computer interaction, data mining, computer graphics, and information visualization as applied to visual sense-making and analytics.
• Practical experience with a number of popular public-domain data analysis and visualization packages and libraries.

• Matthew Ward, Georges Grinstein, Daniel Keim. Interactive Data Visualization: Foundations, Techniques, and Applications, 2nd ed., A K Peters/CRC Press, 2015.
• Charu Aggarwal, Data Mining: The Textbook, Springer, 2015. 

• 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

CSE332