Students gain advanced knowledge of survival analysis; statistical methods for genetics, bioinformatics, epidemiology and environmental research; longitudinal data analysis; analysis of biological shapes and trajectories; generalized linear models, estimating equations, model selection and bioassay; and design for biological and medical studies. Students graduate with the qualitative and quantitative skills necessary for professional research and teaching in providing stochastic models and methods, algorithms and graphical tools for the analysis of data for a variety of life-sciences fields.