Courses

The Department of Biostatistics offers courses for 3 degree programs within the department (PhD, MS, and MPH) as well as courses for students from other departments and programs. Students in the Department of Biostatistics also take courses offered by the Department of Statistics and the College of Public Health and Health Professions.

Course Offerings

Department Schedule (Grid) 

MS and PhD Courses

All courses in the MS and PhD programs require three semesters of calculus and one semester of linear algebra.

GMS 6827 – Advanced Clinical Trials (3)
This course covers the statistical principles and methods used in the design and analysis of clinical trials. Topics include group sequential designs, adaptive clinical trials, and Statistical Monitoring of Clinical Trials.  Syllabus

PHC 6020 – Clinical Trials Methods (3)
This course will introduce some basic statistical concepts and methods used in Epidemiology and will focus on the statistical principles and methods used in clinical trials, including phase I to IV clinical trials. Although the class will have an emphasis on phase III trials, we will also discuss the feature and statistical issues in phase I and II clinical trials. For phase III trials, we will discuss ways of treatment allocation that will ensure valid inference on treatment comparison. Other topics include sample size calculation, survival analysis, early stopping of a clinical trial, and noncompliance. Syllabus

PHC 6050c – Biostatistical Methods I (3)
This course is the first in a two-course sequence that provides students with the fundamentals of biostatistical data analysis. The main emphasis of the course is on linear models, focusing on the theory and practice of regression and analysis of variance. Specific topics include simple and multiple regression for quantitative and categorical data, random effects models for correlated data, factorial and block designs, and nonparametric regression. Students will learn to use the statistical package R for data analysis. Syllabus

PHC 6051 – Biostatistical Methods II (3)
Biostatistical data analysis using generalized linear models, generalized linear mixed models, semiparametric and nonparametric regression, and neural networks; theory and practice in the health sciences. Syllabus

PHC 6063 – Biostatistical Consulting (3)
This course covers communication, management, organization, computational, and biostatistical thinking skills necessary for consulting in biostatistics. Syllabus

PHC 7068 – Biostatistical Computing (3)
This course is intended to develop your ability to perform statistical computing. This course will prepare students to be able to implement statistical methods and learn how different algorithms work. Upon successful completion of the course, students should be able to convert an algorithm into a workable program and write functions that others can use and understand; construct a simulation study and use it to evaluate the size and power of a statistical test or method; use resampling techniques such as the bootstrap and cross-validation to assess model fit and compare competing models; implement computational methods for optimization (e.g., Newton-Raphson, gradient descent), numerical integration (e.g., Monte Carlo integration), and regression (e.g., LASSO) and also learn basic Bayesian computation methods. Syllabus

PHC 6084 – Bayesian Biostatistical Methods (3) 
To equip students with an understanding of the basics of Bayesian statistics, with special emphasis on practical implementation. Syllabus

PHC 6099 – Programming Basics for Biostatistics (3)
The Introduction to Biostatistical Computing course is intended to develop your programming skills to perform statistical computing. The course will focus on both R programming language (using the RStudio interface) and Python programming language (using the Anaconda interface), both of which are free and open-source software programs. The R language part will cover programming topics including vectorization, data input and output, data visualization (ggplot2), data manipulation (tidyverse), building R packages, and building R Shiny applications. R markdown will be used for direct integration and dynamic reporting. The Python language part will cover programming topics including object-oriented programming, scientific computing (numpy), data manipulation (pandas), data visualization (matplotlib), and text mining. The Jupyter Notebook will be used for direct integration and dynamic reporting. Basic statistical inferences (hypothesis testing and linear regression model) will be included for both R and Python languages. In addition, this course will introduce GitHub as the version control system and will also include the use of high-performance computing resources at the University of Florida such as HiPerGator. Syllabus

PHC6937 – Nonparametric Statistics for Public Health and Medical Research (3)This is a MS-level course in nonparametric statistics, which covers a broad range of methods and their applications in public health and medical research. These include nonparametric analogs of the one- and two-sample t-tests and analysis of variance; the sign test, median test, Wilcoxon’s tests, and the Kruskal-Wallis and Friedman tests, tests of independence; nonparametric regression and nonparametric density estimation; modern nonparametric techniques; nonparametric confidence interval estimates. The methods and tools learned from this course will enhance students’ abilities in data analysis, and professional advancement. All applications of methods in this course will be implemented using R statistical software. This course is an elective intended for MS/PhD students in biostatistics but also open for MS/PhD students in other PHHP and COM departments, especially for whom the nonparametric data analysis are common in their research and future career.Prerequisites:   Permission of the instructor. Syllabus

PHC6937 – Statistical and Computational Analysis of Genomic Data (3)
The course is designed to be a master-level elective course but is also open to MS/PhD students, who are interested in bioinformatics/computational biology. The course will focus on statistical and computational methods/software on next-generation sequencing data analysis. Specific topics include (i) Using R/Bioconductor packages to handle common types of genomic data; (ii) DNA-seq, DNA methylation, and metagenomics; (iii) RNA-seq, ChIP-seq, ATAC-seq, and Hi-C; (iv) Single-cell genomics (RNA-seq, CITE-seq, spatial transcriptome). The course will include both didactic class time to learn skills, as well as lab time to hone them. Learning in the course is primarily assessed by three homework assignments and a final course project. Syllabus

PHC6937 – Clinical Trial Practice (3)This course covers statistical design and analysis of real trials sponsored by the NIH, DoD and pharmaceutical industry. Topics include trial objectives, study outcome and trial design selection, sample size determination, statistical analysis plan development, statistical monitoring of clinical trials, among others.  Syllabus

PHC 6937 – Analysis of Multivariate Data (3)
This course covers linear models methodology including simple and multiple regression and analysis of variance including factorial and block designs. The course covers regression for categorical data, random effects models for correlated data, and nonparametric and semiparametric regression. Syllabus

PHC 6937 – Analytic Methods for Infectious Diseases (3)
This course will introduce concepts of infectious disease epidemiology and study designs and analytic methods for evaluating interventions. Especially the relation between the underlying transmission dynamics and the design and evaluation of interventions will be discussed. Special emphasis will be on the design and evaluation of vaccination and vaccination programs. We will present methods for real-time statistical evaluation of interventions of emerging infectious diseases. Statistical and mathematical methods include survival analysis, likelihood methods, stochastic processes, network theory, and stochastic and deterministic transmission models. Examples include case studies in influenza, Ebola, dengue, Zika, cholera, and others. Presentations are largely statistical and mathematical but with a focus on concepts. Syllabus

PHC 6937 – Stochastic Epidemic Modeling (1)
The student will learn the theory and applications of modeling epidemic outbreaks and statistical inference for such. The focus will however be on methodology. The theory involves deterministic models, usually presented with sets of differential equations, and stochastic models. Large population properties will be derived using probabilistic methods such as central limit theory, branching process theory, theory for population processes, l random graph theory, and coupling. Statistical methods will also be presented using e.g. martingales, counting processes, and the likelihood theory.  Syllabus

PHC6937 – Advanced Survival Analysis (3)
Theoretical introduction to statistical inferential procedures useful for analyzing randomly right-censored failure time data. Syllabus

PHC 6937 – Infectious Disease Data Analysis (3)
Infectious disease data arises from complex mechanisms, including transmission, surveillance, and the accrual of immunity. The goal of this course is to introduce common statistical approaches to the analysis of infectious disease data, with a particular focus on developing the underlying models describing disease transmission, and on methods for parameter estimation. The focus of this course will be on generally applicable methods, but we will use a variety of diseases as case studies, including COVID-19, dengue virus, measles, and other diseases of humans and wildlife. Syllabus

PHC 7056 – Longitudinal Data Analysis (3)
Likelihood-based and semiparametric methods for longitudinal data and methods to deal with missing data in both settings. Discussion of the impact of missing data both theoretically and practically on inference, and approaches to conduct sensitivity analysis for inference. Syllabus

Courses for Students Not in MS or PhD Programs in Biostatistics

These courses do not require mathematical prerequisites, but they do require prerequisites that are other biostatistical courses for non-biostatistics majors.

Public Health Courses

PHC 6410—Psychological, Behavioral, and Social Issues in Public Health (3) 
Health behavior from an ecological perspective; includes primary, secondary, and tertiary prevention across a variety of settings; incorporates behavioral science theory and methods.

PHC 6937 – Introduction to Public Health (3)
The purpose of this course is to provide a broad introduction to public health as well as an understanding of how their PhD specializations contribute to achieving the goals of public health.  A full syllabus for this course can be found in the archives here: http://mph.ufl.edu/current-students/courses/syllabus-archives/.

PHC 6089 – Public Health Computing  (3) – Formerly PHC6055, PHC6080, PHC6081
This is a three-credit course that covers using SAS and R to manage and analyze public health data. Students will learn how to import, modify, visualize, and perform common analyses of public health data using SAS and R. Syllabus