In the College of Agriculture and Life Sciences .
Quantitative prediction and interpretation are increasingly essential components of biological, physical, and social sciences. Complex patterns, structures, and interactions raise fundamental and fascinating questions that can be addressed only using mathematical, statistical, and computational methods. The wealth of data that can be acquired using modern methodologies to address these questions, in turn, requires substantive quantitative approaches to make possible appropriate analysis and interpretation. Computational power, meanwhile, continues to increase exponentially, providing the means for sophisticated analysis of complex phenomena.
The Biometry and Statistics major, in the Department of Statistics and Data Science, focuses on the application of statistical and mathematical techniques to the sciences. Statistics and Data Science is concerned with quantitative aspects of scientific investigation: design, measurement, summarization of data, and reaching conclusions based on probability statements. Students with ability in mathematics and an interest in its applications will find this a rewarding and challenging major.
The work of an applied statistician or data scientist can encompass research, teaching, consulting, and computing in almost any combination and in a wide variety of fields of application. Opportunities for employment are abundant in academics, government, and businesses ranging from large corporations to small firms; salaries are usually excellent. Experience gained through summer employment, undergraduate research, or work as an undergraduate teaching assistant is highly recommended. For further details on the Biometry and Statistics major/minor, please contact Dr. Martin Wells (1190 Comstock Hall), at firstname.lastname@example.org or go to stat.cornell.edu.
M. T. Wells, chair (1198 Comstock Hall, (607) 255-8801, 255-1646); S. Basu, J. Booth, F. Bunea, S. Das, T. DiCiccio, A. El Alaoui, J. Entner, J. Guinness, G. Hooker, M. E. Karns, K. Kato, N. Kiefer, D. Matteson, F. Molinari, Y. Ning, M. Nussbaum, K. Packard, D. Ruppert, M. Smith, Y. S. Wang, M. Wegkamp, X. D. Yang, X. Yang