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 6088 – Statistical Analysis of Genetic Data (3) 
An introduction to statistical procedures in human and animal genetics, including Hardy-Weinberg equilibrium, basic linkage analysis, linkage disequilibrium, and association with disease. The goal is to prepare students for potential research in statistical genetics and genomics. Syllabus

PHC 6089 – Public Health Computing  (3)
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 (online)  Syllabus 

PHC 6092 – Introduction to Biostatistical Theory (3)
Concepts and principles of statistical theory, including probability and random variables, parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, statistical decision theory, and linear models. Syllabus

PHC 6097 – Statistical Learning with Applications in Health Sciences (3)
This course should be useful to second-year master’s students or PhD students in biostatistics or related fields as preparation for research and professional advancement. This course covers a broad range of statistical learning methods that are useful for modern data analysis, specifically in the analysis of high-dimensional data (p<n). Many of these methods go far beyond the classical statistical methods (e.g., linear regression) and are developed for addressing various problems (e.g., non-linearity) we encounter in real situations. Statistical learning methods covered in this class also include some newly developed methods, such as deep learning, which has achieved great success in many areas (e.g., computer vision and natural language processing). We will demonstrate the use of these methods with applications, especially in the context of health science research. Syllabus

PHC 6790 – Biostatistical Methods Using SAS (3)
The purpose of this course is to introduce and prepare students for biostatistical computing using the SAS statistical software. It builds on the knowledge obtained in the Biostatistical Methods I and II courses by reinforcing the material and focusing on application within the SAS framework. Topics covered include data management, frequency tables, linear and non-linear models, longitudinal data analysis, Matrix programming, simulation, and using SAS macros. Syllabus 

PHC 6791 – Biostatistical Graphics & Data Visualization (3)
The world is growing increasingly reliant on collecting and analyzing information to help people make decisions. Because of this, the ability to communicate effectively about data is an important component of future job prospects across nearly all disciplines. In this course, students will learn the foundations of information visualization and sharpen their skills in communicating using health science data. Throughout the semester, we will use R and other software to explore concepts in graphic design, storytelling, data wrangling and plotting, and biostatistics as they apply to data-driven communication. Whether you’re an aspiring data scientist or you just want to learn new ways of presenting health science information, this course will help you build a strong foundation in how to talk to people about data. 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 – Applied Longitudinal Data Analysis (3)
Concepts and methods for longitudinal data analysis based on randomized trials and observational studies. Syllabus

PHC 6937 – Advanced Bayesian Methods (3)
This course introduces students to advanced concepts of Bayesian biostatistics with special emphasis on practical implementation in bioassay, measurement error, survival analysis, longitudinal studies, and spatial statistics. Students will be able to fit Bayesian models to analyze real data sets using freely available software such as WinBUGS and R. Syllabus

PHC 6937 – Causal Inference (3)
Concepts and methods for causal inference from randomized trials and observational studies. Syllabus

PHC 6937 – Deep Learning Research (3)
This course will learn deep learning methods and research skills that are necessary for new deep learning method development and modern data analysis. The methods and tools learned from this course can enhance students’ ability in data analysis, method development, and professional advancement. Syllabus

PHC 6937 – Frontiers in Biostatistics  (3)
This course will introduce biostatistics Master and PhD students to current issues and methods in modern biostatistics research. Current faculty will present selected topics from their current research. Syllabus

PHC6937 – Introduction to Statistical Learning (3)
This course gives a brief introduction to commonly used methods (e.g., trees and deep learning) in statistical/machine learning.  These methods have been increasingly used in modern data analysis to address various problems (e.g., biomedical problems) we encounter in real-world situations. Syllabus

PHC 6937 – Stochastic Modeling (3)
The student will learn both the theory and practice of stochastic processes and modeling.  This will include the theory of random phenomena that is concerned with the flow of events in time and space, especially those exhibiting highly variable behavior that can be described by probability distributions.  Specifically, the student will learn to deal with the branching process, random walks, martingales, Markov processes, Poisson process, counting processes, and birth and death processes as applied to the health sciences biology.  Many of the examples and illustrations of the methods will be in the area of infectious diseases.  There will be an emphasis on learning methods of strong scientific importance as opposed to purely mathematical theory.  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

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

PHC 7066 – Large Sample Theory (3)
Detailed introduction to large sample theory and its application in univariate and multivariate parametric and nonparametric estimation. Syllabus

PHC 7090 – Advanced Biostatistical Methods I (3)
Theory and application for estimation and hypothesis testing for independent data using linear models. Principles of frequentist and bayesian estimation and inference. Application using statistical software. Writing data analysis reports. Syllabus

PHC 7091 — Advanced Biostatistical Methods II (3)
Theory and application for independent and dependent data using generalized linear models and generalized linear mixed models. Bayesian and Frequentist inference. Application using statistical software. Writing data analysis reports. Syllabus

PHC 7925 – Biostatistics Journal Club (1)
This class will meet weekly to present, review and discuss current articles in biostatistics or statistics journals or discipline-specific (e.g. medicine, public health, epidemiology) articles with substantive biostatistical content. Syllabus 

PHC 7979 – Advanced Research (Variable)

PHC 7980 – Research for Doctoral Dissertation (Variable)

STA 6177 – Applied Survival Analysis (3)
This course covers survival analysis, Kaplan-Meier estimates, proportional hazards model, related tests, phase I, II, and III clinical trials, designs, and protocols. Syllabus (Online) Syllabus (Campus) 

STA 7179 – Advanced Survival Analysis (3)
Theoretical introduction to statistical inferential procedures useful for analyzing randomly right-censored failure time data.

Courses for Students Not in MS or PhD Programs in Biostatistics

PHC 4094 – Introduction to Biostatistics for Health Science and Public Health (3)
This 3-credit course provides an introduction to some concepts and methods of biostatistical data analysis that are widely used in health sciences and public health. The topics include analysis of variance to compare three or more population means, correlation, simple linear regression, multiple linear regression, nonparametric and distribution-free statistical methods, and some basic concepts about survival analysis. Public health examples are used for demonstration. Students will practice preparing and interpreting data analysis reports. Syllabus (M, W)  Syllabus (T, R)

PHC 4792 – Data Visualization in the Health Sciences (3)
In this course, students will learn the foundations of information visualization and sharpen their skills in communicating using public health data. Throughout the semester, we will primarily use R to explore concepts in graphic design, storytelling, data wrangling and plotting, biostatistics, and artificial intelligence as they apply to data-driven communication. Syllabus

PHC 6050 – Statistical Methods Health Science 1 (3)
This course covers statistical vocabulary, methods for descriptive data analysis, the fundamentals of probability and sampling distributions, methods for statistical inference and hypothesis testing based on one or two samples, categorical data analysis, and linear regression. Data analysis will be conducted in SPSS. Syllabus

PHC 6052 – Introduction to Biostatistical Methods (3)
Introduction to the concepts and methods of biostatistical data analysis. Topics include descriptive statistics, probability, standard probability distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, power and sample size estimation, one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data, and simple linear regression. SAS statistical software for data management, statistical analysis, and power calculations. Syllabus (On-Campus, Fischer) Syllabus (Online, Fischer) 

PHC 6075 – Biostatistical Literacy (3)
This course covers concepts and techniques, including survival data, multiple-group comparisons, and non-linear regression, necessary to read, interpret, and critically evaluate statistical results in health science literature relevant to the interests of the student. This course offers no formal training in statistical software. Syllabus

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

PHC 6022 – Design and Conduct of Clinical Trials (3)
This course focuses on various study designs, including phase I-IV, single-arm, crossover, factorial, and sequential multi-stage, plus the means to allocate study participants to appropriate treatment groups using randomization (blocked or stratified) and prognostic factors. In addition, the protection of study participants and the need for equipoise is covered, including regulatory restrictions and the latest patient privacy regulations for the dissemination and use of data associated with the participants in clinical trials. The importance of informed consent and the use of intent-to-treat analysis will also be emphasized. Syllabus

PHC 6053 – Regression Methods for Health and Life Sciences (3)
This course introduces graduate students in fields other than statistics to a wide range of modern regression methods. Emphasis is on modeling driven by actual data from studies in a variety of areas, primarily from health, biology, and ecology. The primary topics are multiple linear regression, logistic regression, and Poisson regression. A main goal is to learn what approach to use among the linear and nonlinear models, and how to determine whether the fit is adequate. By the end of the course, students will achieve competency in carrying out the analyses in SAS. Syllabus

PHC 6059 – Introduction to Applied Survival Analysis (3)
This course covers survival analysis, Kaplan-Meier estimates, proportional hazards model, related tests, phase I, II, and III clinical trials, designs, and protocols. Syllabus

PHC 6064 – Survey of Advanced Biostatistical Methods for the Health Sciences (3)
This course uniquely blends the fundamentals of inference with an introduction to advanced statistical techniques critical for the analysis of today’s ever-growing body of health-related data. Topics span the analysis of high-dimensional, categorical, and longitudinal data, and applications are undertaken with the statistical software R and SAS. Syllabus

PHC 6937 – Survey of Biostatistical Methods (3)
This course is a survey of biostatistical methods beyond one and two sample techniques covered in PHC 6052. Advanced topics will be selected from areas such as multiple linear regression, study design, ANOVA, contingency tables, logistic regression, Poisson regression, repeated measures, longitudinal data analysis, missing data methods, model/variable selection, survival analysis, multivariate methods, or non-parametric methods. The focus will be on the application of these techniques to data from the health sciences. Examples will make use of SAS and R for this course. Syllabus

Public Health Courses

The following courses offered by the MPH program in the College of Public Health and Health Professions are taken by MPH, MS, and PhD students in the Department of Biostatistics.

PHC 6001—Principles of Epidemiology in Public Health (3)
Overview of epidemiology methods used in research studies that address disease patterns in community and clinic-based populations. Includes distribution and determinants of health-related states or events in specific populations and application to control of health problems.

HSA 6114—Introduction to the U.S. Health Care System (3) – This course is designed to familiarize students with basic concepts and ideas concerning the distribution of health and illness, the organization of the health care system, and the relationship of one to the other. Definitions of health and illness, as well as the historical context for developments of our health care system, are discussed and debated. The course concludes with a discussion of trends that could impact the healthcare system in the future. Students should come to class ready to discuss and debate the major themes related to health and distribution of disease, the ability of the U.S. healthcare system to meet the needs of the population, as well as the policy environment that influences access to healthcare services.

PHC 6313—Environmental Health Concepts in Public Health (3 or 2)
Survey of major topics of environmental health. Sources, routes, media, and health outcomes associated with biological, chemical, and physical agents in the environment. Effects of agents on disease, water quality, air quality, food safety, and land resources. Current legal framework, policies, and practices associated with environmental health and intended to improve public health.

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:

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