Master of Science in Biostatistics
Admissions and Requirements
Courses You’ll Take
Tuition & Fees
Admissions and Requirements
To be accepted to this program, you must have:
A Bachelor's degree
A bachelor’s degree in Mathematics, Statistics, Computer Science or a related field from an accredited college or university
3 GPA or above on a 4.0 scale
Applicants need to have taken undergraduate courses in calculus, multivariable calculus, linear algebra and introductory statistics
Taken the TOEFL
(Only required if English is not your native language)
A written personal statement
A one-page personal statement describing the applicants’ interest in and potential for contributing to the field of biostatistics, career objectives, self-assessment of computer, quantitative analysis, personal skills and general preparation for succeeding in a biostatistics MS program.
3 letters of recommendation
At least one of the references must be academic (from a professor, instructor or faculty mentor).
Official transcripts from all previous schools
Note: The GRE is required for applicants whose overall GPA is less than 3.0 on a 4.0 scale or have received a C in one or more of the pre-requisite courses. Many factors are considered in evaluating an individual's application, but many successful applicants will have verbal, quantitative, and analytic writing GRE scores at or above the 40th percentile.
Resume or CV
Courses You’ll Take
|Course Number||Course Name||Credits|
|BIOS 801||Biostatistics Theory I||3|
This course is designed to prepare students in the Master of Science in Biostatistics to have a solid understanding of the probabilistic tools and language (at a rigorous and advanced calculus level) needed as a foundation of biostatistical inference. Major topics to be covered include probability theory, transformations and expectations of random variables, families of distributions, random vectors, sampling distributions and convergence. Prerequisite: Calculus I, II and III, or equivalent courses; and instructor permission.
|BIOS 802||Biostatistics Theory II||3|
This course is designed to prepare Masters students in Biostatistics to have a solid understanding of biostatistical inference. Major topics to be covered include random samples, data reduction, point estimation, hypothesis testing, interval estimation, and prediction for common parametric models. Prerequisite: BIOS 801, or an equivalent course and instructor permission.
|BIOS 810||Introduction to SAS Programming||3|
An introduction to programming for statistical and epidemiologic analysis using the SAS Software System. Students will learn to access data from a variety of sources (e.g. the web, Excel, SPSS, data entry) and create SAS datasets. Data management and data processing skills, including concatenation, merging, and sub-setting data, as well as data restructuring and new variable construction using arrays and SAS functions will be taught. Descriptive analysis and graphical presentation will be covered. Concepts and programming skills needed for the analysis of case-control studies, cohort studies, surveys, and experimental trials will be stressed. Simple procedures for data verification, data encryption, and quality control of data will be discussed. Accessing data and summary statistics on the web will be explored. Through in-class exercises and homework assignments, students will apply basic informatics techniques to vital statistics and public health databases to describe public health characteristics and to evaluate public health programs or policies. Laboratory exercises, homework assignments, and a final project will be used to reinforce the topics covered in class. The course is intended for graduate students and health professionals interested in learning SAS programming and accessing and analyzing public use datasets from the web. Prerequisite: BIOS 806 or an equivalent introductory statistics course, EPI 821 and permission of instructor.
|BIOS 815||Biostatistical Computing||3|
This course is designed for graduate students who are interested in statistical computing. The course will introduce graduate students to the R statistical language, PYTHON and their uses in biostatistical computing. Topics include introductory R, data management and manipulation, loops, vectorizing code, writing functions, coding shiny apps, pipe operators, coding numerical methods, resampling methods, data simulation and data visualization. In addition, students will be introduced to PYTHON and the R reticulate package for harnessing the power of PYTHON from within R. Prerequisite: BIOS 806, BIOS 810 or Instructor permission
|BIOS 818||Biostatistical Linear Models: Methods and Application||3|
This course is designed to prepare the graduate student to analyze continuous data and interpret results using methods of linear regression and analysis of variance (ANOVA). The major topics to be covered include simple and multiple linear regression model specification and assumptions, specification of covariates, confounding and interactive factors, model building, transformations, ANOVA model specification and assumptions, analysis of covariance (ANCOVA), multiple comparisons and methods of adjustment, fixed and random effect specification, nested and repeated measures designs and models, and diagnostic methods to assess model assumptions. Interpretation of subsequent analysis results will be stressed. Concepts will be explored through critical review of the biomedical and public health literature, class exercises, an exam, and a data analysis project. Statistical analysis software, SAS (SAS Institute Inc., Cary, NC, USA.), will be used to implement analysis methods. The course is intended for graduate students and health professionals who will be actively involved in the analysis and interpretation of biomedical research or public health studies. Prerequisite: Permission of instructor, calculus (including differential and integral calculus), BIOS 806 or BIOS 816 or an equivalent statistics course, BIOS 810 or equivalent experience with SAS programming.
|BIOS 823||Categorical Data Analysis||3|
Survey of the theory and methods for the analysis of categorical response and count data. The major topics to be covered include proportions and odd ratios, multi-way contingency tables, generalized linear models, logistic regression for binary response, models for multiple response categories and log-linear models. Interpretation of subsequent analysis results will be stressed. Prerequisite: Permission of instructor; BIOS 816 or equivalent course work.
|BIOS 824||Survival Data Analysis||3|
The course teaches the basic methods of statistical survival analysis used in clinical and public health research. The major topics to be covered include the Kaplan-Meier product-limit estimation, log-rank and related tests and the Cox proportional hazards regression model. Interpretation of subsequent analysis results will be stressed. Prerequisite: Permission of instructor, calculus (including differential and integral calculus); BIOS 806 or BIOS 816 or an equivalent statistics course; BIOS 810 or equivalent experience with SAS programming.
|BIOS 829||Introduction to Biostatistical Machine Learning||3|
This course is designed to prepare graduate students to use modern statistical learning methods for modeling and prediction from data. Major topics to be covered include linear regression, classification (logistic regression, linear and quadratic discriminant analysis, K-Nearest Neighbors), resampling methods (cross-validation, the bootstrap), linear model selection and regularization (subset selection, shrinkage methods, dimension reduction), nonlinear approaches (polynomial regression, splines, Generalized Additive Models), tree-based methods (Classification and Regression Trees, bagging, random forests, boosting), support vector machines, unsupervised learning (principal component analysis, clustering). The mathematical level of this course is modest, with only simple matrix operations. An introduction to the statistical programming language R will be provided. Prerequisite: At least one multivariate statistics course, eg BIOS 818, BIOS 823, BIOS 824, BIOS 825 or equivalent; BIOS 815 Biostatistical Computing; or equivalent courses and Instructor permission.
|CPH 500||Foundations in Public Health||3|
This is an introductory survey course, which will ensure that all public health students, within their first full year of study, are exposed to the fundamental concepts and theories that provide the basis for the body of knowledge in the field of public health. This course will prepare students to work in public health with a sound theoretical, conceptual, and historical basis for their work. CPH 500 must be successfully completed in the first 21 hours of the program of study.
|Course Number||Course Name||Credits|
|BIOS 825||Correlated Data Analysis||3|
A survey of the theory and methods for analysis of correlated continuous, binary and count data. Major topics to be covered include linear models for longitudinal continuous data, generalized estimating equations, generalized linear mixed models, impact of missing data, and design of longitudinal and clustered studies. Interpretation of subsequent analysis results will be stressed. Concepts will be explored through critical review of the biomedical and public health literature, class exercises, two exams and a data analysis project. Computations will be illustrated using SAS statistical software (SAS Institute Inc., Cary, NC, USA.). The course is intended for graduate students and health professionals who will be actively involved in the analysis and interpretation of biomedical research or public health studies. Prerequisite: Permission of instructor and BIOS 823.
|BIOS 835||Design of Medical Health Studies||3|
This course is designed to prepare the graduate student to understand and apply principles and methods in the design of biomedical and public health studies, with a particular emphasis on randomized, controlled clinical trials. The major design topics to be covered include sample selection, selecting a comparison group, eliminating bias, need for and processes of randomization, reducing variability, choosing endpoints, intent-to-treat analyses, sample size justification, adherence issues, longitudinal follow-up, interim monitoring, research ethics and non-inferiority and equivalence hypotheses. Data collection and measurement issues also will be discussed. Communication of design approaches and interpretation of subsequent analysis results also will be stressed. Concepts will be explored through critical review of the biomedical and public health literature, class exercises and a research proposal. The course is intended for graduate students and health professionals interested in the design of biomedical or public health studies. Prerequisite: Permission of Instructor, BIOS 806 or an equivalent introductory statistics course.
|BIOS 896||Research Other Than Thesis in Biostatistics||3|
This course is for more advanced students who wish to pursue their research interests in selected areas of Medical Humanities.
|BIOS 898||Special Topics in Biostatistics||3|
A course designed for Masters students that focuses on selected topics or problems in Biostatistics.
|EPI 820||Epidemiology in Public Health||3|
The objective of the course is to understand the application of survey and research methodology in epidemiology, especially in the community setting. Theoretical aspects will be taught as an integral part of understanding the techniques of study design and community survey. Concepts to be covered include measure of disease occurrence, measures of disease risk, study design, assessment of alternative explanations for data-based findings and methods of testing or limiting alternatives. Students will be expected to address an epidemiological question of interest to them, first developing the hypothesis and conducting a literature search, then developing a study design and writing, in several stages, a brief proposal for the study.
|EPI 845||Epidemiologic Methods||3|
This course is primarily designed for graduate students and health professionals interested in learning in-depth epidemiologic concept and methods. Methods covered in this course include approaches to minimize random and systematic error, advanced screening methods, systematic reviews and meta-analyses, nested case-control and case-cohort studies, matched case-control and cohort studies, clinical trials, longitudinal epidemiologic studies and analyses of national surveys with multistage complex sampling. Students will practice their skills using SAS and RevMan on simulated and actual research data. Prerequisite: BIOS 806; EPI 820; BIOS 810 strongly recommended.
|EPI 945||Analytic Epidemiologic Methods||3|
This course is designed primarily for graduate and professional students interested in performing analyses of epidemiologic data. Topics include analyses of multinomial and longitudinal data, multiple imputation, Poisson regression and meta analysis. Students will practice their skills by performing SAS analyses of simulated and actual research data. Corequisite: BIOS 825. Prerequisite: EPI 820; EPI 821; EPI845; BIOS 806; BIOS 818; BIOS 823 and BIOS 810. Students should consult their academic advisor if other coursework or experience qualifies as a prerequisite.
Tuition & Fees
Per Credit Hour
3 Credit Hours
Out of State Residents
Per Credit Hour
3 Credit Hours
- Fall SemesterJun 01