CONCEPTS AND TOOLS FOR DATA ANALYSIS AND VISUALIZATION

LIS 407

Introduction to data visualization including how and why visualization can be an effective tool for summarizing, analyzing and communicating about data, the limitations and challenges in using data visualizations, including misrepresentation and bias and planning appropriate types of visualization(s) based on source data, audience, and goals. Instruction will include hands-on experience with popular visualization software platforms to develop visualizations and presentations.

COMPSCI 565

Introduction to topics such as perception, cognition, communication, design, implementation, applications, tools, and evaluation. Provides a broad survey of the field and covers fundamental concepts, theory, and tools in data visualization with opportunities for hands-on activities. Gain real-world experience in designing and evaluating visualizations.

STAT 436

Techniques for visualization within data science workflows. Topics include data preparation; exploratory data analysis; spatial, tabular, and graph structured data; dimensionality reduction; model visualization and interpretability; interactive queries and navigation.

LIS 707

Introduces key concepts in data visualization and communication including how and why visualization can be an effective tool for summarizing, analyzing and communicating about data, and limitations and challenges of using visualization techniques. Students will use contemporary software to develop visualization dashboards and presentations as well as plan appropriate types of visualization(s) based on source data, audience, and goals, evaluate visualizations for effectiveness and bias.

COMPSCI 765

Principles of the visual presentation of data. Survey of Information Visualization, Scientific Visualization, and Visual Analytics. Design and evaluation of visualizations and interactive exploration tools. Introduction to relevant foundations in visual design, human perception, and data analysis. Encodings, layout and interaction. Approaches to large data sets. Visualization of complex data types such as scalar fields, graphs, sets, texts, and multi-variate data. Use of 2D, 3D and motion in data presentations. Implementation issues.