This course is an introduction to the elements of data science. Topics include data visualization, data wrangling, statistical learning and predictive analytics, text mining and spatial data.
Individualized investigation under the direct supervision of a faculty member. (Minimum of 37.5 clock hours required per credit hour.)
Special Notes
Maximum concurrent enrollment is two times.
This course introduces three classes of multivariate statistical methods: factor analysis, multidimensional scaling (MDS), and cluster analysis emphasizing exploratory applications and learning about the structure of a data matrix.
A course on the application and practice of statistics in Natural and Health Sciences. Courses covers using modern computational tools: inferential statistics, multivariate regression, experimental design, ANOVA and non-parametric statistics.
This is a project course in data science and related fields. Interdisciplinary teams will analyze a new data science problem, develop a model, and control for error and overfitting.
Students gain experience applying a broad range of statistical techniques to real-world data, interpreting findings, and communicating results through the completion of a project.
Explores advanced topics in statistics not included in existing courses, reflecting specific interests of students and instructors.
A substantial independent project in the area of statistics, carried out by the student with the guidance of a faculty mentor.
Special Notes
Consent of instructor required.