Key Courses - MPH in Biostatistics
Key Courses - MPH in Biostatistics
RSPH Core Requirements
Provides the student with basic knowledge about the behavioral sciences as they are applied to public health. Content includes an overview of each discipline and current issues for students who are not enrolled in the BSHE MPH Program.
Department of Behavioral, Social, and Health Education Sciences
Prerequisite/concurrent: BIOS 500. Emphasizes the concepts and premises of the science of epidemiology. Methods of hypothesis formulation and evaluation are stressed. Techniques for quantifying the amount of disease (or other health indicator) in populations are introduced, followed by discussion of epidemiologic study designs useful for identifying etiologic factors and other relevant correlates of disease. Students gain facility with the calculation of basic epidemiologic measures of frequency, association, and impact. The concepts of random variability, bias, and effect modification are examined in detail. The use of stratified analysis, including Mantel-Haenszel techniques, is explored. Inferences from study results are discussed. Students are required to analyze and critique studies from the current medical and scientific literature.
Department of Epidemiology
EH 500 is a survey course designed to introduce public health students to basic concepts of environmental sciences, to the methods used to study the interface of health and the environment, to the health impacts of various environmental processes and exposures, and to the public health approach to controlling or eliminating environmental health risks. To address these concepts, basic environmental health principles (exposure assessment, environmental toxicology, environmental epidemiology, risk assessment), as well as specific environmental health issues including water and air pollution, hazardous chemical/waste exposures, climate change, and environmental drivers of disease ecology, will be covered.
Gangarosa Department of Environmental Health
Pre-requisites: GEH, GH, and GLEPI students may not enroll unless with departmental permission.
The overarching objective of GH 500 is to equip students with critical perspectives and resources that they will need as public health professionals and global citizens in our increasingly inter-connected and interdependent world. The course introduces students to: (1) fundamental cross-cutting themes that contextualize contemporary global health issues; and (2) selected health topical areas such as maternal and child health, pandemics, and non-communicable diseases. The course provides an overview of the past, present, and expected future directions of global health.
Hubert Department of Global Health
Required for all MPH students. Introduces students to the US health care system, both the public and private sector. Examines the structure of the health system, current topics in health care reform, the policy process, and advocacy for public health.
Department of Health Policy and Management
1 hour online module addressing 4 of the 12 CEPH required Foundational Knowledge items. The module will begin with an introduction to a "Public Health Perspective followed by the 4 items of foundational knowledge.
PUBH students will join students from health professional programs across the Woodruff Health Sciences Center to receive didactic training to perform effectively on interprofessional teams and to apply leadership and management principles to address a relevant public health issue. Interprofessional teams will compete in a health challenge competition designed to address public health and clinical issues of importance to the Atlanta community.
Biostatistics Requirements
In addition to the courses below, students must take at least 4 credit hours of electives to meet a minimum of 42 credit hours total.
Prerequisite: Multivariate Calculus (Calculus III) or permission of instructor. This course is a mathematically sophisticated introduction to the concepts and methods of biostatistical data analysis. The topics include descriptive statistics; probability; detailed development 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. The course will also equip students with computer skills for implementing these statistical methods using standard software SAS and R.
Department of Biostatistics and Bioinformatics
This is the first regression analysis course in applied statistics designed for BIOS MPH students. 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, confounding and interaction, residual and influence diagnostics, variable transformations, multicollinearity, model selection and validation. The second part of the course includes: generalized linear models, including logistic regression, nominal and ordinal logistic regression, and Poisson regression. Scientific interpretation of results will be emphasized throughout the course. Students are expected to use R (or SAS if preferred), when necessary, for homework assignments and projects. Prerequisites: Coursework in statistics up to and including an introduction to simple linear regression (BIOS 506 or equivalent). Familiarity with basic concepts of probability, statistical inference, and linear algebra (e.g., matrix calculation) is needed for successful completion of the course.
Department of Biostatistics and Bioinformatics
Prerequisites: Multivariate Calculus (Calculus III) or permission of instructor. Introduction to Probability, random variables, distributions, conditional distributions, expectations, moment generating functions, order statistics, and limiting distributions.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 510 or permission of instructor. Fundamental concepts in statistical inference will be covered including: statistical models, sampling distributions, standard errors, mean square errors, method of moments, maximum likelihood estimation, asymptotic normality, confidence intervals, hypothesis tests, Wald tests, likelihood ratio tests, power analysis, p-values, multiple comparisons. Common frameworks for inference will be discussed including: parametric/non-parametric/Bayesian inference, the delta method, bootstrap, permutation tests.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 506, BIOS 507, BIOS 510, and BIOS 511 or permission of instructor. This course provides 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 tables, log-rank tests, Cox regression models, and parametric regression models. The emphasis is on practical implementation of standard methods using SAS or R and interpretation of results.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 507 or permission of instructor. This course introduces students to regression techniques commonly used in analyzing longitudinal and multilevel data that are frequently encountered in biomedical and public health research. This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course focuses on data analysis and interpretation. Students will gain practical experience using R for statistical computing.
Department of Biostatistics and Bioinformatics
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.