Department of Biostatistics (GRAD)
The Department of Biostatistics is recognized as a worldwide leader in research and practice. Members of the faculty are interested both in the development of statistical methodology and application of statistics in applied research. The research strengths include: development of new statistical methods to address pressing issues in medicine and public health sciences; design of innovative clinical trials that allow faster evaluation of new therapeutic agents; collaborative work focused upon important public health concerns, including infectious diseases, cancer, cardiovascular disease, obesity and drinking water safety; and utilization of strong quantitative skills to improve the health of human beings around the globe.
The mission of the Department of Biostatistics is to forge dramatic advances in health science research that benefit human health in North Carolina, the U.S., and globally through the development of profound and paradigm-shifting innovations in biostatistical methodology and the thoughtful implementation of biostatistical practice to solve public health problems.
Master of Public Health (M.P.H.)
The redesigned UNC Gillings School of Global Public Health’s master of public health (M.P.H.) program is for people who are passionate about solving urgent local and global public health problems. With a legacy of outstanding education, cutting edge research and globally recognized leadership, the UNC Gillings School is creating the next generation of public health leaders through our integrated training program and 21st century curriculum. The Department of Biostatistics hosts the Public Health Data Science concentration.
Master of Science (M.S.)
The master of science (M.S.) degree in the Department of Biostatistics provides students with research-oriented training in the theory and methodology of biostatistics and its application to solving problems in the health sciences.
Doctor of Philosophy (Ph.D.)
The doctor of philosophy (Ph.D.) degree in the Department of Biostatistics provides advanced, research-oriented training in theory and methodology of biostatistics to prepare individuals for careers in academia, government, and industry.
Following the faculty member's name is a section number that students should use when registering for independent studies, reading, research, and thesis and dissertation courses with that particular professor.
Kevin Anstrom, Clinical Trials, Statistical Consulting, Causal Inference, Data Safety Monitoring, Pragmatic Clinical Trials, and Coordinating Center Operations
Jianwen Cai (93), Survival Analysis and Regression Models, Clinical Trials, Analysis of Correlated Responses
David J. Couper (77), Epidemiological Methods, Longitudinal Data, Data Quality
Michael Hudgens (42), Nonparametric Estimation, Group Testing, Causal Inference, Infectious Diseases
Joseph G. Ibrahim (11), Bayesian Inference, Missing Data Problems, Bayesian Survival Analysis, Generalized Linear Models, Genomics
Anastasia Ivanova (83), Clinical Trials Design, Sequential Design of Binary Response Experiments, Statistical Methodology in Biostatistics
Gary G. Koch (14), Categorical Data Analysis, Nonparametric Methods
Michael R. Kosorok (88), Biostatistics, Bioinformatics, Empirical Processes, Statistical Learning, Data Mining, Semiparametric Inference, Monte Carlo Methods, Survival Analysis, Clinical Trials, Personalized Medicine, Cancer, Cystic Fibrosis
Lisa M. LaVange (45), Data Science, Clinical Trials, Regulatory Science, Analysis of Complex Survey Data
Yun Li (59), (Joint with the Department of Genetics), Statistical Genetics
Danyu Lin (31), Survival Analysis, Semiparametric Statistical Methods, Clinical Trials
Yufeng Liu (73), (Joint with the Department of Statistics and Operations Research), Statistical Machine Learning and Data Mining, High-Dimensional Data Analysis, Nonparametric Statistics and Functional Estimation, Bioinformatics, Design and Analysis of Experiments
James Stephen Marron (82), (Joint with the Department of Statistics and Operations Research), High Dimension Low Sample Size (HDLSS), Data and/or Data, Exotic Data Types such as Manifold and Tree-Structural Data
Andrew Nobel (Joint with the Department of Statistics and Operations Research), Data Mining, Statistical Data of Genomic Data, Machine Learning
John S. Preisser Jr. (89), Categorical Data, Longitudinal Data Analysis
Bahjat Qaqish (94), Generalized Linear Models, Survival Analysis, Statistical Computing
Todd A. Schwartz (13), Categorical Data, Clinical Trials
Richard Smith (Joint with the Department of Statistics and Operations Research), Spatial Statistics, Time Series Analysis, Extreme Value Theory, Bayesian Statistics
Kinh N. Truong (90), Time Series Analysis, Nonparametric Regression, Bootstrap Methods, Hazard Regression, Splines
Donglin Zeng (5), High Dimensional Data, Survival Analysis
Haibo Zhou (40), Missing/Auxiliary Data, Survival Analysis, Human Fertility
Hongtu Zhu (48), Neuroimaging Statistics, Structural Equation Models, Statistical Computing, Diagnostic Methods
Fei Zou (4), Statistical Genetics
Robert Agans (78), Population-Based Research Methods, Multimode Data Collection Procedures, Questionnaire Development, Standardization and Validation, Hard-to-Reach Populations and Minorities
Jamie B. Crandell (64),(Joint with the School of Nursing), Bayesian Methods, Longitudinal Analysis and Measurement Error Modeling
Tanya P. Garcia (67), Survival Analysis, Semiparametric Theory, Longitudinal Data Analysis
Annie Green Howard (75), Cardiovascular Disease, Global Health
Quefeng Li (81), High Dimensional Data Analysis, Integrative Analysis of Omics Data, Robust Statistics, Factor Models
Feng-Chang Lin (71), Survival Analysis, Generalized Linear Models, Longitudinal Analysis, Hearth Disease and Stroke, Infectious Disease, Neuroscience
Jane Monaco (43), Survival Analysis, Correlated Failure Time Data
Naim Rashid (79), Cancer, Genomics, High Throughput Sequencing, High Dimensional Data Analysis, Variable Selection Selection
Daniela T. Sotres-Alvarez (74), Linear Mixed Models, Latent Variable Models, Dietary and Physical Activity Patterns
Xianming Tan (50), Finite Mixture Models, Design of Clinical Studies, Variable Selection for Zero-Inflated Models, Non-Parametric Regression
Di Wu (51), (Joint with the School of Dentistry) Statistical Bioinformatics and Biostatistics for Preprocess and Integration of High-Dimensional Biomedical Data
Yuchao Jiang (91), Statistical Modeling, Method Development and Data Analysis in Genetics and Genomics
Michael I. Love (39), (Joint with the Department of Genetics), Statistical Modeling of Genetics Data, High-Throughput Sequencing, RNA Sequencing (RNA-seq), Empirical Bayes Methods
Matthew A. Psioda (86), Bayesian Trial Design, Computational and Statistical Epigenomics, Bayesian Computation
Bonnie Shook-Sa (98), Causal Inference, Survey Sampling, Infectious Diseases, Epidemiology
Baiming Zou (97), Robust Modeling of Data with Complex Structures, Machine Learning Methods for Large Scale Electronic Health Record Data Analysis
Jane Eslinger (62)
Vincent Toups (17)
Adjunct Assistant Professors
Shrikant I. Bangdiwala
Lloyd E. Chambless
Clarence E. Davis
James E. Grizzle
Ronald W. Helms
Lawrence L. Kupper
Keith E. Muller
Dana E. Quade
Pranab K. Sen
Michael J. Symons
Craig D. Turnbull
Advanced Undergraduate and Graduate-level Courses
Access to SAS, Excel required. Permission of instructor for nonmajors. Introductory course in probability, data analysis, and statistical inference designed for B.S.P.H. biostatistics students. Topics include sampling, descriptive statistics, probability, confidence intervals, tests of hypotheses, chi-square distribution, 2-way tables, power, sample size, ANOVA, non-parametric tests, correlation, regression, survival analysis.
Required preparation, previous or concurrent course in applied statistics. Permission of instructor for nonmajors. Introduction to use of computers to process and analyze data, concepts and techniques of research data management, and use of statistical programming packages and interpretation. Focus is on use of SAS for data management and reporting.
Students will gain proficiency with R, data wrangling, data quality control and cleaning, data visualization, exploratory data analysis, with an overall emphasis on the principles of good data science, particularly reproducible research. The course will also develop familiarity with several software tools for data science best practices, such as Git, Docker, Jupyter, Make, and Nextflow.
Arrangements to be made with the faculty in each case. A course for students of public health who wish to make a study of some special problem in the statistics of the life sciences and public health. Honors version available.
Required preparation, knowledge of basic descriptive statistics. Major topics include elementary probability theory, probability distributions, estimation, tests of hypotheses, chi-squared procedures, regression, and correlation.
Topics will include gaining proficiency with R and Python, data wrangling, data quality control and cleaning, data visualization, exploratory data analysis, and introductory applied optimization, with an overall emphasis on the principles of good data science, particularly reproducible research. Some emphasis will be given to large data settings such as genomics or claims data. The course will also develop familiarity with software tools for data science best practices, such as Git, Docker, Jupyter, and Nextflow.
This course will be an introductory course to machine learning. The goal is to equip students with knowledge of existing tools for data analysis and to get students prepared for more advanced courses in machine learning. This course is restricted to SPH Master of Public Health students.
Course is designed to meet the needs of health care professionals to appraise the design and analysis of medical and health care studies and who intend to pursue academic research careers. Covers basics of statistical inference, analysis of variance, multiple regression, categorical data analysis. Previously offered as PUBH 741. Permission of instructor.
Continuation of BIOS 641. Main emphasis is on logistic regression; other topics include exploratory data analysis and survival analysis. Previously offered as PUBH 742.
Required preparation, basic familiarity with statistical software (preferably SAS able to do multiple linear regression) and introductory biostatistics, such as BIOS 600. Continuation of BIOS 600. Analysis of experimental and observational data, including multiple regression and analysis of variance and covariance. Previously offered as BIOS 545. Permission of the instructor for nonmajors.
Required preparation, two semesters of calculus (such as MATH 231, 232). Fundamentals of probability; discrete and continuous distributions; functions of random variables; descriptive statistics; fundamentals of statistical inference, including estimation and hypothesis testing.
Required preparation, three semesters of calculus (such as MATH 231, 232, 233). Introduction to probability; discrete and continuous random variables; expectation theory; bivariate and multivariate distribution theory; regression and correlation; linear functions of random variables; theory of sampling; introduction to estimation and hypothesis testing. Students may not receive credit for both BIOS 660 and BIOS 672.
Distribution of functions of random variables; Helmert transformation theory; central limit theorem and other asymptotic theory; estimation theory; maximum likelihood methods; hypothesis testing; power; Neyman-Pearson Theorem, likelihood ratio, score, and Wald tests; noncentral distributions. Students may not receive credit for both BIOS 661 and BIOS 673.
Principles of study design, descriptive statistics, sampling from finite and infinite populations, inferences about location and scale. Both distribution-free and parametric approaches are considered. Gaussian, binomial, and Poisson models, one-way and two-way contingency tables.
Required preparation, BIOS 662. Matrix-based treatment of regression, one-way and two-way ANOVA, and ANCOVA, emphasizing the general linear model and hypothesis, as well as diagnostics and model building. Reviews matrix algebra. Includes statistical power for linear models and binary response regression methods.
Fundamental principles and methods of sampling populations, with emphasis on simple, random, stratified, and cluster sampling. Sample weights, nonsampling error, and analysis of data from complex designs are covered. Practical experience through participation in the design, execution, and analysis of a sampling project.
Introduction to the analysis of categorized data: rates, ratios, and proportions; relative risk and odds ratio; Cochran-Mantel-Haenszel procedure; survivorship and life table methods; linear models for categorical data. Applications in demography, epidemiology, and medicine.
Analysis of variance and multiple linear regression course at the level of BIOS 663 required. Familiarity with matrix algebra required. Univariate and multivariate repeated measures ANOVA, GLM for longitudinal data, linear mixed models. Estimation and inference, maximum and restricted maximum likelihood, fixed and random effects.
Statistical concepts in basic public health study designs: cross-sectional, case-control, prospective, and experimental (including clinical trials). Validity, measurement of response, sample size determination, matching and random allocation methods.
Provides a foundation and training for working with data from clinical trials or research studies. Topics: issues in study design, collecting quality data, using SAS and SQL to transform data, typical reports, data closure and export, and working with big data.
Source and interpretation of demographic data; rates and ratios, standardization, complete and abridged life tables; estimation and projection of fertility, mortality, migration, and population composition.
Required preparation, three semesters of calculus. Introduction to probability; discrete and continuous random variables; combinatorics; expectation; random sums, multivariate distributions; functions of random variables; theory of sampling; convergence of sequences, power series, types of convergence, L'Hopital's rule, differentiable functions, Lebesgue integration, Fubini's theorem, convergence theorems, complex variables, Laplace transforms, inversion formulas.
Distribution of functions of random variables; central limit theorem and other asymptotic theory; estimation theory; hypothesis testing; Neyman-Pearson Theorem, likelihood ratio, score, and Wald tests; noncentral distributions. Advanced problems in statistical inferences, including information inequality, best unbiased estimators, Bayes estimators, asymptotically efficient estimation, nonparametric estimation and tests, simultaneous confidence intervals.
Introduction to concepts and techniques used in the analysis of time to event data, including censoring, hazard rates, estimation of survival curves, regression techniques, applications to clinical trials.
Field/topical/research seminar. Instructors use this course to offer instruction in particular topics or approaches.
Field visits to, and evaluation of, major nonacademic biostatistical programs in the Research Triangle area. Field fee: $25.
Directed research. Written and oral reports required.
Directed research. Written and oral reports required.
Permission of the department for students with passing grade of either doctoral qualifying examination in biostatistics. BIOS 700 will introduce doctoral students in biostatistics to research skills necessary for writing a dissertation and for a career in research.
Required preparation, one undergraduate-level programming class. Teaches important concepts and skills for statistical software development using case studies. After this course, students will have an understanding of the process of statistical software development, knowledge of existing resources for software development, and the ability to produce reliable and efficient statistical software.
Permission of the instructor. Statistical theory applied to special problem areas of timely importance in the life sciences and public health. Lectures, seminars, and/or laboratory work, according to the nature of the special area under study.
This graduate-level course concentrates on up-to-date views of intercellular signal processing, with emphasis on signal transduction mechanisms as they relate to cellular/physiological responses in both normal development and disease. Signaling mechanisms that will be discussed include autocrine, paracrine, juxtacrine signaling and cell-matrix interactions.
This course will introduce the methods used in clinical. Topics include dose-finding trials, allocation to treatments in randomized trials, sample size calculation, interim monitoring, and non-inferiority trials.
Theory and application of nonparametric methods for various problems in statistical analysis. Includes procedures based on randomization, ranks and U-statistics. A knowledge of elementary computer programming is assumed.
Topics include correlograms, periodograms, fast Fourier transforms, power spectra, cross-spectra, coherences, ARMA and transfer-function models, spectral-domain regression. Real and simulated data sets are discussed and analyzed using popular computer software packages.
Measure space, sigma-field, measurable functions, integration, conditional probability, distribution functions, characteristic functions, convergence modes, SLLN, CLT, Cramer-Wold device, delta method, U-statistics, martingale central limit theorem, UMVUE, estimating function, MLE, Cramer-Rao lower bound, information bounds, LeCam's lemmas, consistency, efficiency, EM algorithm.
Elementary decision theory: admissibility, minimaxity, loss functions, Bayesian approaches. Hypothesis testing: Neyman-Pearson theory, UMP and unbiased tests, invariance, confidence sets, contiguous alternatives. Elements of stochastic processes: Poisson processes, renewal theory, Markov chains, martingales, Brownian motion.
Linear algebra, matrix decompositions, estimability, multivariate normal distributions, quadratic forms, Gauss-Markov theorem, hypothesis testing, experimental design, general likelihood theory and asymptotics, delta method, exponential families, generalized linear models for continuous and discrete data, categorical data, nuisance parameters, over-dispersion, multivariate linear model, generalized estimating equations, and regression diagnostics.
Continuation of BIOS 664 for advanced students: stratification, special designs, multistage sampling, cost studies, nonsampling errors, complex survey designs, employing auxiliary information, and other miscellaneous topics.
Theory and application of methods for categorical data including maximum likelihood, estimating equations and chi-square methods for large samples, and exact inference for small samples.
Presents modern approaches to the analysis of longitudinal data. Topics include linear mixed effects models, generalized linear models for correlated data (including generalized estimating equations), computational issues and methods for fitting models, and dropout or other missing data.
Required preparation, integral calculus. Life table techniques; methods of analysis when data are deficient; population projection methods; interrelations among demographic variables; migration analysis; uses of population models.
The course will review major statistical methods for the analysis of MRI and its applications in various studies.
Fundamental concepts, including classifications of missing data, missing covariate and/or response data in linear models, generalized linear models, longitudinal data models, and survival models. Maximum likelihood methods, multiple imputation, fully Bayesian methods, and weighted estimating equations. Focus on biomedical sciences case studies. Software packages include WinBUGS, SAS, and R.
Introductory overview of statistical learning methods and high-dimensional data analysis. Involves three major components: supervised or unsupervised learning methods, statistical learning theory, and statistical methods for high-dimensional data including variable selection and multiple testing. Real examples are used.
Statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. Measures for quantifying test accuracy. Statistical procedures for estimating and comparing these quantities, including regression modeling. Real data will be used to illustrate the methods. Developments in recent literature will be covered.
This course will consider drawing inference about causal effects in a variety of settings using the potential outcomes framework. Topics covered include causal inference in randomized experiments and observational studies, bounds and sensitivity analysis, propensity scores, graphical models, and other areas.
Permission of the instructor. A detailed presentation of natality models, including necessary mathematical methods, and applications; deterministic and stochastic models for population growth, migration.
Topics include Bayes' theorem, the likelihood principle, prior distributions, posterior distributions, predictive distributions, Bayesian modeling, informative prior elicitation, model comparisons, Bayesian diagnostic methods, variable subset selection, and model uncertainty. Markov chain Monte Carlo methods for computation are discussed in detail.
Counting process-martingale theory, Kaplan-Meier estimator, weighted log-rank statistics, Cox proportional hazards model, nonproportional hazards models, multivariate failure time data.
An introduction to statistical procedures in human genetics, Hardy-Weinberg equilibrium, linkage analysis (including use of genetic software packages), linkage disequilibrium and allelic association.
This course provides a comprehensive survey of the statistical methods for the designs and analysis of genetic association studies, including genome-wide association studies and next-generation sequencing studies. The students will learn the theoretical justifications for the methods as well as the skills to apply them to real studies.
Molecular biology, sequence alignment, sequence motifs identification by Monte Carlo Bayesian approaches, dynamic programming, hidden Markov models, computational algorithms, statistical software, high-throughput sequencing data and its application in computational biology.
Clustering algorithms, classification techniques, statistical techniques for analyzing multivariate data, analysis of high dimensional data, parametric and semiparametric models for DNA microarray data, measurement error models, Bayesian methods, statistical software, sample size determination in microarray studies, applications to cancer.
Theory and applications of empirical process methods to semiparametric estimation and inference for statistical models with both finite and infinite dimensional parameters. Topics include bootstrap, Z-estimators, M-estimators, semiparametric efficiency.
An introduction to the statistical collaborative process and leadership skills. Emphasized topics include problem solving, study design, data analysis, ethical conduct, teamwork, career paths, data management, written and oral communication with scientists and collaborators.
Under supervision of a faculty member, the student interacts with research workers in the health sciences, learning to abstract the statistical aspects of substantive problems, to provide appropriate technical assistance, and to communicate statistical results.
This seminar course is intended to give students exposure ot cutting edge research topics and hopefully help them in their choice of a thesis topic. It also allows the student to meet and learn from major researchers in the field.
Using lectures and group exercises, students are taught where and how biostatisticians can offer leadership in both academic and nonacademic public health settings.
Required preparation, a minimum of one year of graduate work in statistics. Principles of statistical pedagogy. Students assist with teaching elementary statistics to students in the health sciences. Students work under the supervision of the faculty, with whom they have regular discussions of methods, content, and evaluation of performance.
Permission of the instructor. Seminar on new research developments in selected biostatistical topics.
Individual arrangements may be made by the advanced student to spend part or all of his or her time in supervised investigation of selected problems in statistics.
Master of Public Health (M.P.H.) Public Health Data Science Concentration
The Public Health Data Science concentration, one of the first applied data science programs situated within a school of public health, gives students the skills and knowledge to employ cutting-edge data science tools and respond to pressing public health issues with effective solutions. Data science draws upon multiple disciplines, combining the statistical skills to manipulate data and make inferences, the mathematical skills to model phenomena and make predictions, and the computer science skills to manage and analyze large data sets. Steeped in the public health context, our program offers a unique focus on leveraging the foundational statistical, mathematical, and computer science elements of data science to generate useful information from data sources relevant to public health.
Requirements for the M.P.H. degree in the Public Health Data Science concentration
|M.P.H. Integrated Core|
|SPHG 711||Data Analysis for Public Health Fall 1||2|
|SPHG 712||Methods and Measures for Public Health Practice Fall 1||2|
|SPHG 713||Systems Approaches to Understanding Public Health Issues Fall 1||2|
|SPHG 701||Leading from the Inside-Out Spring 1||2|
|SPHG 703||MPH Pre-Practicum Assignments Spring 1||0.5|
|SPHG 721||Public Health Solutions: Systems, Policy and Advocacy Spring 1||2|
|SPHG 722||Developing, Implementing, and Evaluating Public Health Solutions Spring 1||4|
|Practicum: 200 minimum hours Summer 1|
|SPHG 704||MPH Post-Practicum Assignments Fall 2||0.5|
|BIOS 650||Basic Elements of Probability and Statistical Inference I Fall 1||3|
|BIOS 512||Data Science Basics Fall 1||3|
|BIOS 635||Introduction to Machine Learning Spring 1||3|
|BIOS 645||Principles of Experimental Analysis Spring 1||3|
|EPID 710||Fundamentals of Epidemiology Fall 2||3|
|Elective (Graduate-level courses)||3|
|Elective (Graduate-level courses)||3|
|Elective (Graduate-level courses)||3|
|M.P.H. Culminating Experience|
|BIOS 992||Master's (Non-Thesis) Spring 2||3|
Students will develop the following Public Health Data Science competencies, building on the foundational public health knowledge they attain in the Gillings M.P.H. Integrated Core courses.
|PHDS01.||Describe how data from a variety of public health data sources could be manipulated for appropriate summary and analysis.|
|PHDS02.||Select and use data visualization methods to interpret and communicate research results, with the overall objective of conducting reproducible research, both individually and in project teams.|
|PHDS03.||Select and utilize appropriate data analysis and machine learning methods to solve problems and make improvements in a given public health context.|
|PHDS04.||Understand, evaluate, and constructively address potential sources of sampling bias and other biases and key sources of uncertainty in data driven health research.|
|PHDS05.||Provide tools that facilitate the expansion of complex statistics and methods to public health contexts traditionally reticent to move away from more traditional approaches, thereby extending the reach of quantitative and methodological innovations in public health.|
Please visit Applying to the Gillings School first for details and information. Application to the residential M.P.H. is a two-step process. Please apply separately to (1) SOPHAS and (2) UNC–Chapel Hill (via the Graduate School application). Visit https://gradschool.sites.unc.edu/master-of-public-health/ for more details. If you are interested in the online M.P.H., please visit the MPH@UNC website and fill out an inquiry form.
To satisfy degree and accreditation requirements, a Gillings MPH practicum must:
- Be a public health practice experience.
- Allow for the application of graduate-level public health skills.
- Yield at least two student-generated products, produced in the practicum setting for the practicum setting, that allow for demonstration of five M.P.H. Foundational Competencies.
- Be mentored by a supervisor (preceptor) with public health expertise and experience to guide the practicum work.
- Take place in a location approved for student travel (UNC Travel Policy), and the student must complete UNC Gillings International Pre-Departure Travel Requirements prior to travel if applicable.
- Comprise a minimum of 200 hours (equivalent to five weeks of full-time work).
Gillings M.P.H. students must successfully complete SPHG 711, SPHG 712, SPHG 713, SPHG 701, SPHG 721, and SPHG 722 prior to beginning their practicum. For more information, please visit our M.P.H. Practicum web page.
A milestone degree requirement for all graduate students at UNC–Chapel Hill, including M.P.H. students at the Gillings School of Public Health, is the comprehensive exam. The comprehensive exam will cover the public health foundational knowledge and competencies covered in the M.P.H. Core courses: SPHG 711, 712, 713, 721, 722. Students will have an opportunity to demonstrate synthesis and higher order learning of the 22 core competencies achieved in the M.P.H. Core courses during the exam. The exam will be administered and graded by Gillings faculty and clear instructions on how to prepare for and complete the comprehensive exam will be provided. Should students not successfully pass the comprehensive exam a remediation plan will be developed. Students cannot retake the comprehensive exam for 90 days after the initial exam and must be registered in at least one credit while taking the comprehensive exam.
M.P.H. students must have permanent grades in all M.P.H. Core or concentration courses before taking the culminating experience (992) course. An Incomplete in any M.P.H. Core or concentration course will prevent a student from beginning the culminating experience (992) course. Each student completes a 3-credit culminating experience and produces a high-quality written product that is completed in the last term of the program of study. The high-quality written product demonstrates a synthesis of two foundational and two concentration-specific competencies appropriate to the student’s educational and professional goals. This culminating experience ideally is delivered in a manner that is useful to external stakeholders, such as nonprofit or governmental organizations, and could take the form of a course-based capstone project or master’s paper but will be tailored to the concentration a student chooses.
Academic Advising and Faculty Mentoring
We are committed to providing quality academic advising and mentoring for all students. We ensure that M.P.H. students get the guidance they need with several components: 1) an orientation program that provides an overview of the types and sources of M.P.H. advising; 2) cohort advising sessions to disseminate information that is relevant to course planning and registration; 3) faculty mentoring that provides students with tailored support for their academic, professional, personal development, and practicum support.
M.P.H. students will complete a 15-credit-hour Integrated Core taught by an interdisciplinary team of instructors. The 6-credit first semester focuses on understanding public health issues, and the second semester, 8-credit focuses on creating solutions to those issues. Lastly, students will complete a 1-credit Practicum Assignments and Interprofessional Practice Activities course in the second year.
All M.P.H. students complete COMPASS (Core Online Modules to Promote and Accelerate Student Success). These self-paced online modules are open for students prior to their first academic year. Students can complete any and all parts of COMPASS up to and including the first week of class.
Students in the M.P.H. program are required to take 9 credits of elective coursework. Students are expected to use their electives in a thoughtful way to strengthen their public health knowledge/skills and are encouraged to consult with their academic coordinator early prior to the registration period for this purpose. In addition to those courses offered in the Gillings School there are many appropriate electives elsewhere in the University.
For information on policies and procedures, please visit the Gillings School Student Handbook website.
Department of Biostatistics
Lisa M. LaVange
Michael G. Hudgens