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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.


  • W. Bridges: statistical design, applications of mixed models, categorical data analysis
  • P. Gerard: nonparametric density estimation, environmental statistics
  • J. Luo: asymptotics in large p, statistical applications in economics and biology
  • R. Martinez-Dawson: statistics education-assessing statistical literacy, survey design and analysis
  • J. Rieck: reliability, estimation
  • B. Russell: Multi-variate extreme value methods, ecological and environmental applications
  • J. Sharp: statistical computing, experimental design and analysis, biostatistics


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.