Key Courses - MSPH in Data Science in Behavioral, Social, and Health Education Sciences
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.
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.
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.
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.
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.
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.
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.
Data Science BSHES Concentration Requirements
Students with advanced programming experience may replace DATA 534 with BIOS 534, and BIOS 544 with BIOS 545.
Students are also required to take DATA 516.
Students may also take BSHES 735 or BSHES 740 for their BSHES methods selective.
Data Science Core
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.
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.
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.
BSHES Concentration Core
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
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.
Data Science Methods Selectives
The course introduces the use of geographic information systems (GIS) in the analysis of public health data. We develop GIS skills through homework, quizzes, and a case study. Specific skills include map layouts, visualization, and basic GIS operations such as buffering, layering, summarizing, geocoding, digitizing and spatial queries.
Prerequisites: INFO DATA 530 or permission of the instructor. The course continues the use of geographic information systems (GIS) in the analysis of public health data and adds more advanced features. We develop GIS skills through homework, quizzes and a final project, and particularly build upon the skills learned in INFO 530 such as map layouts, visualization, basic spatial statistics, and basic GIS operations such as buffering, layering, summarizing, geocoding, digitizing and spatial queries. We add new topics such as raster analysis open source GIS, (qgis), geo databases, story maps, and making maps in R.
Prerequisites: BIOS 544 or BIOS 545. This course will teach students to use data visualizations to analyze public health, medical, and biological sciences data and communicate information derived from these data to various audiences. Students will learn key concepts and methods in creating data visualizations and put them into practice with hands on assignments creating data visualization and critiquing public health visualizations. Multidisciplinary review and feedback on student designs can help to improve the quality and effectiveness of student visualization, therefore students will often work in pairs or groups.
BSHES Methods Selectives
This data analysis course provides the student with the skills necessary to identify and analytically investigate theory-driven research questions with the goal to investigate behavioral, social and cultural factors that contribute to the health and well-being of individuals, communities, and populations using existing and new databases. In addition, students will learn how to interpret and present data, and communicate findings to a variety of audiences.
This course provides a thorough introduction to qualitative research methods in public health at multiple ecological levels. Students will be introduced to relevant aspects of qualitative research and develop their critical ability to evaluate qualitative methods. Students will undertake their own mini-qualitative studies in order to apply their skills to a public health topic.
This course provides a foundation in designing and conducting health promotion research. Students will learn about various types of research at multiple levels of the social ecological model. The goals of the course include achieving competence in designing studies based on scientifically sound research methodologies and gaining the ability to critically evaluate health promotion research.
Electives
Prerequisites: BIOS 501 or permission of 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.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 500, 506, or 508. This class is designed to cover the concepts and implementations of up-to-date analytic methodologies and strategies in observational studies, and to equip the students with the mindset and essential tools to handle data from observational research either for prediction (statistical learning) or causal inference. Propensity score methods, establishing/validating prediction models, risk stratification, the guidance of Good Research Practice, etc. will be illustrated along with real-life projects and backed up by the recent literatures.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 501 or equivalents and basic programming in R or permission of the instructor. This elective course introduces statistical and machine learning methods for the analysis of single-cell genomic data, with an emphasis on conceptual understanding and practical applications. The course begins with classical statistical methods for bulk transcriptomics and then covers recent statistical and machine learning approaches for single-cell genomics (including transcriptomics and epigenomics) as well as spatial data. Weekly lab sessions are dedicated to practicing the methods introduced in class. Students will learn both the methodological foundations and hands-on workflows for data analysis. By the end of the course, students will be able to perform a complete analyze workflow for single-cell genomics data.
Department of Biostatistics and Bioinformatics
Students learn and apply basic program planning skills, including analysis of the social-ecological and behavioral determinants of a health problem or issue, community assessment, theory-informed intervention design, implementation and evaluation.
In this course, we critically examine the history of public health to gain perspective on current health problems. Students analyze the history of public health institutions, concepts, and practices in the contexts of the history of the social determinants of health, culture, and changing ecologies of health and disease. This course also uses history to analyze health inequities with the goal of promoting health equity.
Prerequisites EPI 530, BIOS 500, EPI 534 and BIOS 591P or BIOS 501 concurrent. This course develops epidemiologic concepts introduced in EPI 530: Epidemiologic Methods I, providing a more advanced discussion of issues related to causality, bias, study design, interaction, effect modification and mediation. It will also provide opportunities for the application of these examples via analysis of epidemiologic data.
Prerequisites EPI 530, BIOS 500, EPI 534, and BIOS 591P concurrent. MSPH and PhD students only.
This course builds on the fundamental epidemiologic concepts introduced in EPI 530: Epidemiologic Methods I. Specifically, causality, bias (including confounding, information bias, and selection bias), and concepts of mediation and interaction will be revisited in greater depth. By the end of the course, students will be able to do the following: formulate research questions to evaluate causality; evaluate the strengths and limitations of epidemiologic studies; assess how the strengths and limitations of a study affect interpretation of study results; utilize epidemiologic methods to address confounding; identify epidemiologic methods to address selection bias and information bias; and calculate measures to assess interaction.
Department of Epidemiology
Experiential Learning
The Practicum 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 Practicum must be supervised by a Field Supervisor and requires approval from a Practicum Advisor designated by the student's academic department at RSPH.
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
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.