Applied Statistics and Data Science
Applied statisticians and data scientists collaborate with scientists in academia, industry and government on the design, implementation and analysis of research studies. This collaboration combines traditional statistical methodology and development, and well as aspects of mathematical sciences such as model development and computation.
Faculty
Faculty involved with applied statistics and data science include:
- Joseph Bible: Longitudinal and clustered data analysis, biostatistics.
- William C. Bridges: statistical design, applications of mixed models, categorical data analysis.
- Patrick Gerard: nonparametric density estimation, environmental statistics.
- Whitney Huang: Statistics of extremes, spatio-temporal statistics, design and analysis of computer experiments, climate/environmental applications, high-frequency physiological data analysis.
- Deborah Kunkel: Bayesian methodology, mixture models, hierarchical models.
- Jun Luo: asymptotics in large p, statistical applications in economics and biology.
- Brook Russell: Multi-variate extreme value methods, ecological and environmental applications.
- Yu-Bo Wang: Bayesian computation and Monte Carlo methodology, causal inference and mediation analysis, and model selection.
Other Resources
Curriculum
The courses in applied statistics and data science focus on design and analysis of experiments, statistical analysis and statistical computing. They allow students to rigorously apply proper statistical methodology to solve real world problems in agriculture, education, engineering, forestry, life sciences and beyond. Students interested in applied statistics and data science can combine course offerings in statistics and other areas of mathematical sciences to develop a deep and broad based understanding of this research area.
