STATISTICAL DATA VISUALIZATION

STAT 433

Perform Data Science as an iterative (back and forth) process of four different types of activities (data collection, data wrangling, data analysis, communication). Traverse through the five requisite stances (scientist, coder, mathematician, methodologist, skeptic). Develop and hone a broad set of computational tools in R (but not the broadest) and a broad set of statistical/machine learning tools (but not the broadest). Focus on doing these with agility to make the coding "transparent" and serve the large goals of the project.

STAT 613

Provides a thorough grounding in modern statistical methods. Introduces statistical techniques and methods of data analysis, including data description, linear regression models, diagnostic tools, prediction and model selection, and experimental design.

STAT 240

Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions.

JOURN/LIS 677

An introduction to information and data visualization: introduction to major concepts, instruction in specific tools for data analysis and visualization, and application of skills in a final project.

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.