Course Sequence - MSPH in Data Science in Behavioral, Social, and Health Education Sciences
Fall 1
Includes 13 total credit hours.
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.
Prerequisites: Data Science students only or by permission of the instructor. In this course, students learn the fundamentals of statistical data science using both traditional statistical methods and programming/simulation-based learning. The topics include study designs, descriptive statistics and data visualizations, applications of conditional and joint probability in data science, simulating random variable distributions, confidence interval estimation and hypothesis testing for means and proportions using a variety of basic inferential one- and two-sample parametric and non-parametric methods. Students successfully completing this course will be able to choose appropriate basic inferential statistical analyses for public health questions, consider ethical implications of data analysis, perform exploratory data analyses and use computational tools to visualize data, produce meaningful reports of statistical analyses in R and provide sound interpretations of analysis results.
Prerequisites: BIOS 500, BIOS 506, BIOS 508 or permission of instructor. In this course, you'll learn about the basic structure of relational databases and how to read and write simple and complex SQL statements and advanced data manipulation techniques. By the end of this course, you'll have a solid working knowledge of structured query language. You'll feel confident in your ability to write SQL queries to create tables; retrieve data from single or multiple tables; delete, insert, and update data in a database; and gather significant statistics from data stored in a database. This course will teach key concepts of Structured Query Language (SQL), and gain a solid working knowledge of this powerful and universal database programming language. This course provides a comprehensive introduction to the language of relational databases: Structured Query Language (SQL). Topics covered include: Entity-Relationship modeling, the Relational Model, the SQL language: data retrieval statements, data manipulation.
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.
Online Asynchronous. 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.
Spring 1
Includes 13 total credit hours.
Students must also take DATA 516: Introduction to Data Science II.
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.
This course will introduce students to how racism operates at multiple ecological levels to create and maintain health inequities and proposed frameworks and approaches to promote health equity. Students will gain an understanding of racism as a public health issue.
Prerequisites: BIOS 500, 506, or 508 and (BIOS 544 or BIOS 545 or EPI 534) or permission of instructor. The elective course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications. The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using R as the programming language.
Prerequisites: BIOS 544 or BIOS 545, R programming experience needed or permission of the instructor. This course is an elective for Masters and PhD students interested in learning some fundamental tools used in modern data science. Together, the tools covered in the course will provide the ability to develop fully reproducible pipelines for data analysis, from data processing and cleaning to analysis to result tables and summaries. By the end of the course students will have learned the tools necessary to: develop reproducible workflows collaboratively (using version control based on Git/GitHub), execute these workflows on a local computer (using command line operations, RMarkdown, and GNU Makefiles), execute the workflows in a containerized environment allowing end-to-end reproducibility (using Docker), and execute the workflow in a cloud environment (using Amazon Web Services EC2 and S3 services). Along the way, we will cover a few other tools for data science including best coding practices, basic python, software unit testing, and continuous integration services.
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.
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.
Fall 2
Includes 9 credit hours of required courses and 2 credit hours of approved electives.
Introduces an array of conceptual theories that posit different patterns of association among a variety of behavioral, psychological, and social antecedents that together can influence health outcomes. The theories covered in this course align with aggregating levels of influence at the individual, interpersonal, organizational/community and macrosocietal levels. In-class discussion and assignments will enable the learner to understand the value of theory for ethical practice, research design, and intervention development, to gain skills in applying theories for program/intervention design, implementation and evaluation.
Provides the student with information and skills related to basic measurement issues involved in assessing variables in health behavior research.
Department of Behavioral, Social, and Health Education Sciences
The thesis requires the conceptualization, design and implementation of an original project resulting in the preparation of a scholarly document. Organized as a directed study with the thesis chair, students develop and refine research questions, conduct a review and analysis of the public health knowledge base, select a theory or organizing framework, formulate a plan for data collection and an IRB application, and draft the initial three chapters of their project.
Department of Behavioral, Social, and Health Education Sciences
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.
Spring 2
Includes 5 credit hours of required courses and 6 credit hours of approved electives.
Enables students to apply the principles and methods learned in an academic setting through the preparation of a scholarly document embodying original research applicable to public health, incorporating a research question that has been successfully evaluated with appropriate analytical techniques and is potentially publishable or has potential public health impact.
Department of Behavioral, Social, and Health Education Sciences
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.