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.

- J Bible: Longitudinal and clustered data analysis, biostatistics
- W. Bridges: statistical design, applications of mixed models, categorical data analysis
- P. Gerard: nonparametric density estimation, environmental statistics
- W. Huang: Statistics of extreemes, spatio-temporal modeling, design and analysis of computer experiments (a.k.a. "Uncertainty Qualification (UQ)")
- D. Kunkel: Bayesian methodology, mixture models, hierarchical models
- J. Luo: asymptotics in large p, statistical applications in economics and biology
- R. Martinez-Dawson: statistics education-assessing statistical literacy, survey design and analysis
- B. Russell: Multi-variate extreme value methods, ecological and environmental applications
- Y. Wang: Bayesian computation and Monte Carlo methodology, causal inference and mediation analysis, and model selection

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.