BIOS 500 (3) Statistical Methods I: Fall. Prerequisite: Algebra. Introduces parametric and nonparametric statistical methodology, including descriptive measures, elementary probability, estimation, hypothesis testing, confidence intervals, common nonparametric methods, and base contingency table analysis. Empirically demonstrates underlying theory. (This course is for informatics and non-bios major students. If does not fulfill any requirements for a biostatistics major student.) Sample Syllabus
BIOS 500 (1) Statistical Methods I Lab: Fall. Prerequisites: Concurrent enrollment in BIOS 500. This lab complements the BIOS 500 course by using hands-on demonstrations of statistical concepts and methods taught in lecture. The statistical software, SAS, will be introduced as a programming tools to accomplish many of these tasks. Sample Syllabus - Labs
BIOS 501 (3) Statistical Methods II: Spring. Prerequisite: BIOS 500 or equivalent. Addresses estimation and hypothesis testing within the context of the general linear model. Examines in depth the analysis of variance, multiple regression, and logistic regression. Previews select advanced techniques. (The course does not fulfill core or elective requirements for biostatistics students.) Sample Syllabus
BIOS 501 (1) Statistical Methods II Lab: Spring. Prerequisites: BIOS 500 and BIOS 500 Lab, and concurrent enrollment in BIOS 501. A continuation of the BIOS 500 Lab. Students learn SAS programming for the statistical methods covered in BIOS 501. Sample Syllabus - Labs
BIOS 502 (2) Statistical Methods III: Fall. Prerequisites: BIOS 500 and BIOS 501. This course introduces students to data analytic methods not covered in BIOS 500 and BIOS 501. It is focused on multilevel models, particularly modeling longitudinal data. Other hierarchical models will also be introduced to analyze other types of clustered data. Students will learn how to specify an appropriate statistical model so that specific research questions of interest can be addressed in a methodologically sound way. Sample Syllabus
BIOS 505 (4) Statistics for Experimental Biology: Spring. Intended for PhD candidates in the biological and biomedical sciences. Introduces the most frequently used statistical methods in those fields, including linear regression, ANOVA, logistic regression, and nonparametric methods. Students learn the statistical skills necessary to read scientific articles in their fields, do simple analyses on their own, and be good consumers of expert statistical advice.
BIOS 506 (4) Foundations of Biostatistical Methods: Fall. Prerequisite: matrix algebra. This course presents a sophisticated introduction to the concepts and methods of biostatistical data analysis. The topics include descriptive statistics; probability; applications of the binomial, Poisson and normal distributions; sampling distributions; point and confidence interval estimation; hypothesis testing; a variety of one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data; and simple linear regression. Sample Syllabus
BIOS 507 (4) Applied Regression Analysis: Spring. Prerequisites: Biostatistics major, BIOS 506 or equivalent; one year of calculus, linear algebra, and matrix algebra. Both theoretical and applied aspects of linear regression and generalized linear regression modeling will be covered in this course. The emphasis will be on applications. The first part of the course covers the following topics: simple linear regression, multiple linear regression, analysis of variance, confounding and interaction, residual and influence diagnostics, variable transformations, multi-collinearity, model selection and validation. The second part of the course includes: generalized linear models, introduction to maximum likelihood estimation, logistic regression, nominal and ordinal logistic regression, Poisson regression. Parameter interpretation and scientific interpretation of results will be emphasized throughout the course. Sample Syllabus
BIOS 508 (4) Biostatistical Methods: The prerequisites include College-level courses in Linear algebra and Calculus and programming experience in either SAS or R (or concurrent enrollment in BIOS 531: SAS Programming.) This course provides a mathematically sophisticated introduction to the concepts and methods of biostatistical data analysis. It aims to provide the students the skills to collaborate with investigators and statistical colleagues in the analysis of data from biomedical and public health studies and to communicate the results of statistical analyses to a broad audience. The topics include descriptive statistics; probability; detailed development of the binomial, Poisson and normal distributions and simulation of random variables from these distributions; sampling distributions; point and confidence interval estimation; simulation studies; hypothesis testing; power analysis and sample size calculations; a variety of one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data and resampling statistics. The course will also equip students with computer skills for implementing these statistical methods using standard statistical software SAS or R.
BIOS 509 (4) Applied Linear Models: The course covers statistical methodology for the analysis of continuous outcome data, primarily from cross-sectional studies and designed experiments. We introduce the key matrix-based methods for estimation and inference based on the multiple linear regression model. Subsequently, topics include secondary hypothesis testing and restrictions, regression diagnostics, model selection, confounding and interaction, analysis of variance and covariance, and an introduction to random effects and the mixed linear model.
BIOS 510 (4) Introduction to Probability Theory: Fall. Prerequisite: calculus and multivariate analysis. Focuses on axiomatic probability, random variables, distribution theory, special parametric families of univariate distributions, joint and conditional distributions, distributions of functions of random variables, and probability modeling.
BIOS 511 (4) Introduction to Statistical Inference: Spring. Prerequisite: BIOS 510. This course provides an introduction to statistical inference. The course is required for Biostatistics MPH students. Fundamental concepts in statistical inference will be covered including: statistical models, sampling distributions, standard errors, asymptotic normality, confidence intervals, hypothesis tests, power analysis. Common frameworks for inference will be discussed including: parametric/likelihood-based inference, the delta method, bootstrap, permutation tests, and Bayesian inference.
BIOS 512 (4) Probability Theory I: Fall. Prerequisite: Calculus and multivariate analysis. Introduction to probability, random variables, distributions, conditional distributions, expectations, moment generating functions, and convergence concepts.
BIOS 513 (4) Statistical Inference I: Spring, Pre-requisite BIOS 512. Introduces the theory of parameter estimation, interval estimation, and tests of hypotheses. In this course, we emphasize the classical "frequentist" (i.e., Neyman-Pearson-Wald) approach to inference. As time permits, we briefly explore alternative paradigms of inference such as neo-Fisherian, Bayesian, and statistical decision theory. This course is required for Biostatistics MSPH students and typically is taken in the second semester of the first year.
BIOS 516 (1) Introduction to Large- Scale Biomedical Data Analysis: Fall. Prerequisite: BIOS 501 or equivalent, or permission from the instructor. This is the overview course for the Bioinformatics, Imaging and Genetics (BIG) concentration in the PhD program of the Department of Biostatistics and Bioinformatics. It aims to introduce students to modern high-dimensional biomedical data, including data in bioinformatics and computational biology, biomedical imaging, and statistical genetics. This course will be co-taught by all BIG core faculty members, with each faculty member giving one or two lectures. The focus of the course will be on the data characteristics, opportunities and challenges for statisticians, as well as current developments and hot areas of the research fields of bioinformatics, biomedical imaging and statistical genetics.
BIOS 520 (2) Clinical Trials Methodology: Spring. Prerequisite: BIOS 500 or BIOS 506. Covers the organization, methodology, and reporting results of clinical trials. Topics covered include conceptualization, data collection, ethical considerations, and protocol adherence and compliance, as well as statistical techniques such as randomization, double-blind techniques, sample size determination, and analysis considerations. Sample Syllabus
BIOS 521 (2) Applied Survival Analysis: Fall. Prerequisites: BIOS 506, BIOS 507, BIOS 510. This course will provide an introduction to statistical concepts and methods related to the analysis of survival data. Topics include survival functions, hazard rates, types of censoring and truncation, life table, log-rank tests, Cox regression models, and parametric regression models. The emphasis is on practical implementation of standard survival analysis methods using SAS or R and results interpretations.
BIOS 522 (2) Survival Analysis Methods: Fall. Prerequisites: BIOS 508, BIOS 509, BIOS 512. Deals with the modern methods used to analyze time-to-event data. Provides background theory, but emphasis is on using methods and interpreting results. Provides coverage of survivorship functions, Kaplan-Meier curves, logrank test, Cox regression, model-fitting strategies, model interpretation, stratification, time-dependent covariates, and introduction to parametric survival models. Computer programs are used. A data analysis project is required. Sample Syllabus
BIOS 524 (2) Introduction to Analytic Methods for Infectious Diseases: Spring.* Prerequisites: BIOS 506 and BIOS 510 or equivalent. Introduces dynamic and epidemiological concepts particular to infectious diseases, including elements of the infection process; transmission patterns; epidemic, endemic, micro and macroparasitic diseases; zoonosis; basic reproduction number; dependent happenings; and effects of intervention. Sample Syllabus
BIOS 526 (3) Modern Regression Analysis: Fall. Prerequisites: BIOS 509 or instructor’s permission. This course introduces students to modern regression techniques commonly used in analyzing public health data. Topics include: (1) parametric and non-parametric methods for modeling non-linear relationships; (2) methods for modeling longitudinal and multilevel data that account for within group correlation; (3) Bayesian regression modeling; and (4) methods for multivariate outcomes. Sample Syllabus
BIOS 531 (2) SAS Programming: Fall. Prerequisites: BIOS 501 or equivalent, or BIOS 506 (concurrent), OR permission of the instructor. This course offers instruction in basic SAS programming. It assumes no prior knowledge of SAS, and begins with an introduction to the data step and procedure call. Topics covered include: dataset manipulation, report writing, arrays, looping, simulation, SAS macro, SAS Interactive Matrix Language (IML), SAS Graphics, and SAS Output Delivery System (ODS). The final exam for the course is the Base SAS Certification exam. Students who pass this exam successfully receive a certificate of completion from the SAS Institute. Sample Syllabus
BIOS 532 (2) Statistical Computing: Spring. Prerequisite: BIOS 531, BIOS 506, and BIOS 510, 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. Sample Syllabus
BIOS 534 (3) Machine Learning: Fall & Spring. Prerequisites: BIOS 500 or equivalent, multivariate calculus, linear algebra, or permission of the instructor. 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.
BIOS 540 (2) Introduction to Bioinformatics: Spring. Prerequisites: BIOS 500, 501, 506, or permission of 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. Sample Syllabus
BIOS 544 (2) Introduction to R programming for Non-BIOS students: Fall. Prerequisites: BIOS 500 and BIOS 501. 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.
BIOS 545 (2) R Programming for BIOS students: Spring. Prerequisites: BIOS 506 or equivalent and basic programming experience using a high level language. This course will introduce students to essential concepts of the R programming with example applications in statistical analysis. Topics include understanding language syntax and data types, constructing informative graphics, authoring functions, learning effective debugging strategies, developing reproducible research documents, and creating packages and documentation for distribution.
BIOS 550 (2) Sampling Applications: Fall and spring. Prerequisite: BIOS 501 or BIOS 506. Focuses on how to select probability samples and analyze the data, using simple random sampling, stratified random sampling, cluster sampling, and multistage sampling. The software package PC-SUDAAN is used for data analysis. Sample Syllabus
BIOS 555 (2) High-Throughput Data Analysis using R and BioConductor: Fall. Prerequisites: BIOS 501 or equivalents. Basic programming experience in R. This course covers the basics of microarray and second-generation sequencing data analysis using R/Bio Conductor 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.
BIOS 560R (VC) Current Topics in Biostatistics: Fall and spring. A faculty member offers a new course on a current topic of interest for both PhD and master’s students. Sample Syllabus
BIOS 570 (2) Introduction to Statistical Genetics: Spring. No prerequisites. This is an introductory course for graduate students in Biostatistics, Bioinformatics, Epidemiology, Genetics, Computational Biology, and other related quantitative disciplines. The course will cover statistical methods for the analysis of family and population based genetic data. Topics covered will include classical linkage analysis, population-based and family‐based association analysis, haplotype analysis, genome‐wide association studies, basic principles in population genetics, imputation-based analysis, pathway‐based analysis, admixture mapping, analysis of copy number variations, and analysis of massively parallel sequencing data. Students will be exposed to the latest statistical methodology and computational tools on gene mapping for complex human diseases.
BIOS 580 (2) Statistical Practice I: Fall. This course will cover topics dedicated to preparing students to collaborate as biostatisticians for public health and biomedical projects with non-statisticians. Covered topics will include consulting versus collaboration, ethics, non-statistical aspects of collaboration (e.g. interpersonal communication), and negotiating expectations with clients. The students will work together in small groups to develop research questions based on an existing real-life datasets and discussions with clinical collaborators, conduct power analyses, choose the appropriate statistical methodology to analyze the research questions, then answer at least one of the questions and present the results in both oral and written format. In addition, individually each student will complete a series of milestones that results in an oral and/or written proposal for an individual capstone project to be completed in the Spring semester.
BIOS 581 (2) Statistical Practice II (Capstone): Spring. This is a required course for the MPH and MSPH students in the Biostatistics and Bioinformatics program in their final spring semester. The purpose of the course is to help students with their capstone project in project management, manuscript writing, and oral presentation while they conduct their project with their individual BIOS advisors. Students will review journal articles to critique study design and statistical analysis methods in a journal club format. They will learn how to write journal articles section by section through lectures and homework assignments. They will develop a manuscript based on their capstone project. At the end of the semester, each student will give an oral presentation on his/her capstone project. Each student will also make a poster on his/her capstone project. Students will receive feedbacks from their peers and instructors to improve their writing and presentation skills. The prerequisite is BIOS 580 - Statistical Practice I.
BIOS 590R (1) Seminar in Biostatistics: Fall and spring. Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings. (Satisfactory/unsatisfactory grading only.)
BIOS 591P (3) Biostatistical Methods II (EPI students only): Spring. Prerequisites: BIOS 500 and BIOS 500 lab. This course covers statistical methodology for the analysis of continuous outcome data, utilizing standard linear modeling approaches. Methods and topics to be discussed include correlation, simple and multiple linear regression, model diagnostics and selection, interaction, analysis of covariance, and one- and two-way analysis of variance (ANOVA).
BIOS 595 Applied Practice Experience: An Applied Practice Experience (APE) is a unique opportunity that enables students to apply practical skills and knowledge learned through coursework to a professional public health setting that complements the student’s interests and career goals. The APE must be supervised by a Field Supervisor and requires approval from an APE Advisor designated by the student’s academic department at Rollins. Registration for the course is required.
BIOS 597R (VC) Directed Study: Fall and spring. Provides in-depth exposure to specific topics not covered in regular courses, for example, statistical genetics and specialized experimental designs.
BIOS 598R (VC) Special Projects: Involves intern-like participation on specific scholarly, research, or developmental projects that expose students to the role of the statistical consultant or collaborator in a variety of research settings.
BIOS 599R (VC) Thesis: Fall and spring. Master’s thesis research.
BIOS 707 (4) Advanced Linear Models: Fall. Prerequisites: BIOS 507, BIOS 511, and a course in matrix algebra. Focuses on generalized inverse of a matrix; vectors of random variables; multivariate normal distribution; distribution theory for quadratic forms of normal random variable; fitting the general linear models by least squares; design matrix of less than full rank; estimation with linear restrictions; estimable functions; hypothesis testing in linear regression; and simultaneous interval estimation.
BIOS 709 (4) Generalized Linear Models: Spring. Prerequisites: BIOS 511 and BIOS 707. Studies analysis of data, using generalized linear models as well as models with generalized variance structure. Parametric models include exponential families such as normal, binomial, Poisson, and gamma. Iterative reweighted least squares and quasi-likelihood methods are used for estimation of parameters. Studies methods for examining model assumptions. Introduces generalized estimating equations (GEE) and quadratic estimating equations for problems where no distributional assumptions are made about the errors except for the structure of the first two moments. Illustrations with data from various basic science, medicine, and public health settings. Sample Syllabus
BIOS 710 (4) Probability Theory II: Fall. Prerequisites: BIOS 510 and BIOS 511. Focuses on axioms of probability, univariate and multivariate distributions, convergence of sequences of random variables, Markov chains, random processes, and martingales. Sample Syllabus
BIOS 711 (4) Statistical Inference II: Spring. Prerequisite: BIOS 710. Examines the fundamental role of the likelihood function in statistical inference, ancillary and sufficient statistics, estimating functions, and asymptotic theory. Presents conditional, profile, and other approximate likelihoods; various ancillary concepts; generalizations of Fisher information in the presence of nuisance parameters; optimality results for estimating functions; and consistency /asymptotic normality of maximum likelihood and estimation function-based estimators. Briefly discuss alternative approaches to inference including Bayesian, Likelihood Principle, and decision theory. Sample Syllabus
BIOS 722 (2) Advanced Survival Analysis: Fall or spring.* Prerequisites: BIOS 510, BIOS 511, and BIOS 706. Provides in-depth coverage of theory and methods of survival analysis, including censoring patterns and theory of competing risks, nonparametric inference, estimating cumulative hazard functions, Nelson estimator, parametric models and likelihood methods, special distributions, two-sample nonparametric tests for censored data, censored data, time-dependent covariates, stratified Cox model, accelerated failure time regression models, grouped survival analysis, multivariate survival analysis, and frailty models. Sample Syllabus
BIOS 723 (4) Stochastic Processes: Fall or spring. * Prerequisites: matrix algebra and BIOS 710. Provides dual coverage of the theory and methods for dealing with the diversity of problems involving branching processes, random walks, Poisson processes, birth and death processes, Gibbs sampling, martingale counting processes, hidden Markov chains, inference on semi-Markov chains, and chain of events modeling. Draws applications from the biological sciences, including the theory of epidemics, genetics, survival analysis, models of birth-migration-death, and the design and analysis of HIV vaccine trials.
BIOS 724 (2) Analytic Methods for Infectious Disease Interventions: Spring. * Prerequisite: BIOS 511. Focuses on advanced analytic, statistical, and epidemiological methods particular to infectious diseases, including analysis of infectious disease data and evaluation of intervention.
BIOS 726 (2) Applied Multivariate Analysis: Fall.* Prerequisites: BIOS 511. Investigates multivariate techniques. Main subjects are inferences about multivariate means, multivariate regression, multivariate analysis of variance (MANOVA) and covariance (MACOVA), principal components, factor analysis, discriminant analysis and classification, and cluster analysis. Demonstrates programs such as SAS and S-Plus. Sample Syllabus
BIOS 731 (2) Advanced Statistical Computing: Fall.* Prerequisites: BIOS 510, 511 and prior programming experience, or permission from one of the instructors. This course covers the theories and applications of some common statistical computing methods. Topics include Markov chain Monte Carlo (MCMC), hidden Markov model (HMM), Expectation-Maximization (EM) and Minorization-Maximization (MM), and optimization algorithms such as linear and quadratic programming. The class has two main goals for students: (1) learn the general theory and algorithmic procedures of some widely used statistical models; (2) develop fluency in statistical programming skills. The class puts more emphasis on implementation instead of statistical theories. Students will gain computational skills and practical experiences on simulations and statistical modeling.
BIOS 732 (2) Advanced Numerical Methods: Fall.* Prerequisites include BIOS 532, BIOS 710 and BIOS 711, or permission of the instructor. BIOS 711 may be taken concurrently. The course covers topics in traditional numerical analysis specifically relevant to statistical estimation and inference. The topics covered include numerical linear algebra, the root finding problem (maximum likelihood) methods such as IRLS, Newton-Raphson, and EM algorithm, and Bayesian techniques for marginalization and sampling for use in statistical inference (MCMC methods). Additional topics may include numerical integration and curve fitting. Sample Syllabus
BIOS 735 (2) Estimating Function Theory: Fall. Prerequisite: BIOS 711 or permission of instructor. Examines topics in the theory of estimating functions. Applications from biomedical studies are used to illustrate the concepts discussed in class. Sample Syllabus
BIOS 736 (2) Statistical Analysis with Missing and Mismeasured Data: Spring.* Prerequisites: BIOS 511 and knowledge of S-plus. For PhD biostatistics students; others must obtain permission of instructor. Introduces concepts and methods of analysis for missing data. Topics include methods for distinguishing ignorable and nonignorable missing data mechanisms, single and multiple imputation, and hot-deck imputation. Computer-intensive methods are used.
BIOS 737 (2) Spatial Analysis of Public Health Data: Spring.* Prerequisites: BIOS 506, 507, 510, 511. Familiarizes students with statistical methods and underlying theory for the spatial analysis of georeferenced public health data. Topics covered include kriging and spatial point processes. Includes a review of recent computational advances for applying these methods.
BIOS 738 (2) Bayesian and Empirical Bayes Methods: Fall.* Prerequisites: BIOS 510 and BIOS 511. Includes Bayesian approaches to statistical inference, point and interval estimation using Bayesian and empirical Bayesian methods, representation of beliefs, estimation of the prior distribution, robustness to choice of priors, conjugate analysis, reference analysis, comparison with alternative methods of inference, computational approaches, including Laplace approximation, iterative quadrature, importance sampling, and Markov Chain Monte Carlo (Gibbs sampling). Various applications, such as small area estimation, clinical trials, and other biomedical applications, will be used.
BIOS 745R (1) Biostatistical Consulting: Fall. Prerequisites: BIOS 507 or 509. This course will cover topics dedicated to preparing doctoral students to lead biostatistical collaborations with non-statisticians in public health, biology, and medicine academic environments. Covered collaboration topics will include consulting versus collaboration, ethics, non-statistical aspects of collaboration (e.g. interpersonal communication), and negotiating expectations with clients. Covered biostatistical topics will include specific aim refinement, appropriate study design for the research question, assessment of feasibility (time and effort) of different statistical methods for the same problem, statistical review of grant proposals including power calculations, and appropriate summarization/presentation of results to non-statistical audiences. Sample Syllabus
BIOS 760R (VC) Current Topics in Biostatistics: Fall and spring. A faculty member offers a new course on an advanced topic of interest, such as spatial analysis, time series, missing data methods, causal inference, and discrete multivariate analysis.
BIOS 770 (2) Advanced Statistical Genetics: Spring.* Prerequisites: BIOS 511, BIOS 570, and BIOS 710, and BIOS 711 or instructor’s permission. This course provides a comprehensive survey of the statistical methods that have been recently developed for the designs and analysis of genetic association studies. Specific topics include genome-wide association studies, likelihood inference and EM algorithm, case-control sampling and retrospective likelihood, secondary phenotypes in case-control studies, haplotypes and un-typed SNPs, population stratification, meta-analysis, multiple testing, winner’s curse, copy number variants, next-generation sequencing studies, rare variants and trait-dependent sampling.
BIOS 777 (1) How to Teach Biostatistics: Fall. Prerequisites: BIOS 507, BIOS 511, and summer TATTO workshop. Prepares students for teaching introductory level courses in biostatistics. The topics discussed are: syllabus development, lecturing, encouraging and managing class discussion, evaluating student performance, test and examinations, cheating, the role of the teaching assistant, teacher-student relationships, teaching students with weak quantitative skills, teaching students with diverse backgrounds, teaching health sciences students, teaching medical students, use of audio-visual techniques, and use of computers. Each student is required to teach a certain subject to the other students and the instructor, followed by a discussion of presentation strengths and weaknesses. Sample Syllabus
BIOS 780R (1) Research Methods in Biostatistics: Spring. Prerequisite: BIOS 511. Acquaints students with a variety of areas of biostatistical research and provides the chance to do preliminary reading in an area of interest. Each student reads a few papers in an area of interest and presents the material to the group. Topics and readings can be suggested by the faculty member in charge or by the students. This course may be repeated for credit. (Satisfactory/unsatisfactory grading only.)
BIOS 790R (1) Advanced Seminar in Biostatistics: Fall and spring. Invited speakers, faculty, and advanced students discuss special topics and new research findings. (Satisfactory/unsatisfactory grading only.)
BIOS 795R (VC) Pre-Candidacy Research: Fall and spring. Provides in-depth exposure to advanced special topics not covered in regular courses.
BIOS 797R (VC) Directed Study: Fall and spring. Provides in-depth exposure to advanced special topics not covered in regular courses.
BIOS 799R (VC) Dissertation: Fall and spring. Dissertation research.
Page Last Updated: 09/12/19