This course provides a structured review of the mathematical and statistical foundations needed for graduate study in biostatistics. Topics include calculus and linear algebra; probability axioms, set operations, conditional probability, and Bayes' rule; discrete and continuous probability distributions; expectation, variance, moment generating functions, covariance, and correlation; joint, marginal, and conditional distributions; independence; and the multinomial and multivariate normal distributions. The course also introduces maximum likelihood estimation and core concepts of estimation theory, including bias, mean squared error, the Cramer-Rao lower bound, and uniformly minimum variance unbiased estimation. Through worked examples and practice problems, students strengthen the mathematical reasoning and problem-solving skills that support subsequent required coursework in biostatistics.
This course introduces emerging artificial intelligence (AI) models for large-scale biomedical data, including genomics, electronic health records, and medical imaging. Students will learn the fundamentals of deep learning architectures and the role of AI is transforming biomedical research. The course emphasizes ethical considerations, the role of statisticians in developing fair and transparent AI models, and in integrating multiple data types (such as text and image) for deeper biomedical insights. The course concludes with a case study on how AI contributed to COVID-19 research. Students will learn to implement deep learning models in Python and apply them for analyzing real life biomedical data.
This course is an introduction to important statistical methods and key concepts for genomic data analysis and their applications to human complex diseases such as cancer. Topics include foundational concepts of the human genome, genome-wide association studies (GWAS), differential expression and methylation analysis, Chip-seq data analysis, single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics data and multi-omic analysis. Practical sessions will involve the use of R, Bioconductor, and other relevant tools.
This course introduces statistical techniques and methods of data analysis, including the use of statistical software (e.g. R). Examples are drawn from the biological, physical, and social sciences. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, basic probability, random variables and expected values, confidence intervals and significance tests for one- and two-sample problems for means and proportions, chi-square tests, linear regression, and analysis of variance.