Vanguard paradigms in biology and biomedicine are forged from complex technological advancements and unparalleled large-scale human experimentation. These paradigms compel researchers to collect copious data and to interpret them as scientific evidence. Biostatisticians routinely encounter data that are complex, high-dimensional and/or large in scale. The number of potential applications and statistical methods are now too vast to memorize. So biostatisticians must rely on a broad framework of mathematical, statistical and scientific principles that will lead them to properly – and reliably – learn from complex data.
Our graduate program emphasizes modern statistical thought and features the foundations of statistical inference, a topic of critical importance when interpreting data as scientific evidence. The program aims to strike a balance between theoretical rigor, methodological proficiency, and functional aptitude. There is a strong emphasis on reproducible, validated research and how to achieve this from a statistical perspective. The curriculum is nondenominational with respect to the foundations of statistical inference (i.e., Frequentist, Bayesian, Likelihood), modern in its emphasis on computing and teaching of statistical principles, and progressive with its emphasis on communication skills.
Biostatisticians must understand the generic construct of data and be able to provide a mathematical framework that transcends the scientific context and generalizes the findings. Our program is designed to impart these critical skills. Well-rounded biostatisticians with thorough methodological training and good communication skills will be well prepared for the challenges that await them.