"Prerequisites: BIOS 509 and BIOS 513 or permission of instructor. This course introduces students to modern regression techniques commonly used in analyzing public health data. Specific topics include: (1) parametric and non-parametric methods for modeling non-linear relationships (e.g., splines and generalized additive models); (2) methods for modeling longitudinal and multi-level data that account for within group correlation (e.g., mixed-effect models, generalized estimating equations); (3) Bayesian methods; and (4) shrinkage methods and bias-variance tradeoffs.

This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course provides a survey of advanced regression approaches with a focus on data analysis and interpretation. Students will gain an understanding of methods that will facilitate future independent and collaborative research for modern research problems. Students will gain practical experience using the R language for statistical computing. "

This class is designed to cover the concepts and implementations of up-to-date analytic methodologies and strategies in observational studies, and to equip the students with the mindset and essential tools to handle data from observational research either for prediction (statistical learning) or causal inference. Propensity score methods, establishing/validating prediction models, risk stratification, the guidance of Good Research Practice, etc. will be illustrated along with real-life projects and backed up by the recent literatures.

Prerequisites: BIOS 500 & BIOS 501 or permission of instructor. This class is designed to help students master statistical programming in SAS. Students in this class will develop programming style and skills for data manipulation, report generation, simulation and graphing. This class does not directly satisfy any competencies as defined by the Department of Biostatistics and Bioinformatics, the Rollins School of Public Health or the Council on Education for Public Health (CEPH). That being said, SAS is a primary data analysis and data management software system in use worldwide, particularly in public health settings. Students who master the skills offered in this course will have a much easier time completing the work for their thesis and will find themselves more ready for a public health career with a more analytical bent.

Prerequisite: BIOS 506, BIOS 510, and BIOS 531 or permission of instructor. Programming style and efficiency, data management and data structures, hardware and software, maximum likelihood estimation, matrix methods and least squares, Monte Carlo simulation, pseudo-random number generation, bootstrap, and UNIX-based computing and graphical methods.

Prerequisites: Multivariate Calculus (Calculus III), Linear Algebra, and Python programming. This course covers fundamental machine learning theory and techniques. The topics include basic theory, classification methods, model generalization, clustering, and dimension reduction. The material will be conveyed by a series of lectures, homeworks, and projects.

Prerequisites: BIOS 500, BIOS 501, or BIOS 506 or permission of the instructor. This course is an introduction to the field of Bioinformatics for students with a quantitative background. The course covers biological sequence analysis, introductions to genomics, transcriptomics, proteomics and metabolomics, as well as some basic data analysis methods associated with the high-throughput data. In addition, the course introduces concepts such as curse of dimensionality, multiple testing and false discovery rate, and basic concepts of networks.

For non-BIOS Students Only. The goal of the course is to will provide an introduction to R in organizing, analyzing, and visualizing data. Once you've completed this course you'll be able to enter, save, retrieve, summarize, display and analyze data.

For BIOS Students Only. This course covers the basic contents of R programming with applications on statistical data analysis. Topics include data types, language syntax, graphics packages, debugging, the tidy verse, efficient programming and package creation.

Prerequisites: BIOS 501 or equivalents and basic programming in R or permission of the instuctor. This course covers the basics of microarray and second-generation sequencing data analysis using R/BioConductor and other open source software. Topics include gene expression microarray, RNA-seq, ChIP-seq and general DNA sequence analyses. We will introduce technologies, data characteristics, statistical challenges, existing methods and potential research topics. Students will also learn to use proper Bioconductor packages and other open source software to analyze different types of data and deliver biologically interpretable results.

A faculty member offers a new course on a current topic of interest for both PhD and Master's students.