DBB Fall 2025 Course Offerings

PUBH 6899. Neural Networks with Applications in Biomedical Research. 3 credits -  This course covers techniques for principles and best practices in neural networks and clustering analysis. It expands the classic regression models based on likelihood to computation-intensive prediction and clustering methods. Various loss functions, estimating equations, and optimization methods such as gradient descent, stochastic gradient descent, and ADAM will be introduced.

PUBH 6899. Generative Artificial Intelligence for Health Data Analysis. 3 credits - This postgraduate online course offers an exploration of how generative artificial intelligence (GAI) can revolutionize health data analysis. Students will learn to leverage tools like ChatGPT and GitHub Copilot to master the fundamentals of biostatistics and health data analytics. The course emphasizes the practical application of large language models (LLMs) to generate Python code for data analysis, including data visualization, statistical modeling, and predictive analytics. By the end of the course, participants will be equipped with GAI skills to enhance their data-driven decision-making in health research and practice. PUBH 6002 is a prerequisite for this course and can be taken concurrently.

PUBH 6899. Microbiome Data Analysis in R. 2 credits - This course is designed for graduate students interested in developing foundational knowledge, analytical skills and experience in microbiome research, including genomics, bioinformatics, programming and biostatistics. Students will learn different R packages to analyze microbiome data and address key questions in microbiome research. This course assumes a basic knowledge of biology (equivalent to BISC - 1116 & 1126), introductory courses in statistics or biostatistics (equivalent to PUBH 6868 or STAT 1127) and R (equivalent to PUBH 6851), and graduate standing.

PUBH 6899. Bayesian Data Analysis. 1 credit - This course introduces state-of-the-art statistical methodology in the analysis of survey data, Bayesian statistics, and missing data in three independent sections. Students are free to choose any one or two or three sections. Section 1 focuses on the analysis of survey data. Section 2 focuses on applied Bayesian statistics and model fitting. Section 3 focuses on missing data analysis. Examples from real-world applications with large-scale data will be used in the class. PUBH 3142. Introduction to Biostatistics for Public Health is a prerequisite.

PUBH 6899. Survey Data Analysis. 1 credit - This course introduces state-of-the-art statistical methodology in the analysis of survey data, Bayesian statistics, and missing data in three independent sections. Students are free to choose any one or two or three sections. Section 1 focuses on the analysis of survey data. Section 2 focuses on applied Bayesian statistics and model fitting. Section 3 focuses on missing data analysis. Examples from real-world applications with large-scale data will be used in the class.  PUBH 3142. Introduction to Biostatistics for Public Health is a prerequisite.

PUBH 6899. Missing Data Analysis. 1 credit - This course introduces state-of-the-art statistical methodology in the analysis of survey data, Bayesian statistics, and missing data in three independent sections. Students are free to choose any one or two or three sections. Section 1 focuses on the analysis of survey data. Section 2 focuses on applied Bayesian statistics and model fitting. Section 3 focuses on missing data analysis. Examples from real-world applications with large-scale data will be used in the class. PUBH 3142. Introduction to Biostatistics for Public Health is a prerequisite.

Health Data Visualization (PUBH 3242 / PUBH 6867) 3 credits - This course introduces health data visualization techniques and approaches for uncovering patterns and communicating insights in scientific fields such as health and biomedical research. Emphasis is placed on storytelling through visual representation, with real-world examples drawn from large-scale health data. Students will explore tools and methods to design effective, meaningful, and interpretable visualizations.
Undergraduate Prerequisite: PUBH 1142
Recommended Background: Basic R programming is helpful but not required.