Course Sequence - MSPH in Data Science
Course Sequence - MSPH in Data Science
Fall 1
Includes 15 total credit hours.
Prerequisites: BIOS 500 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 and data definition statements. Homework will be done using databases running in MySQL which students install on their machines and proc SQL in SAS. Students develop a real-world database project using MySQL during the course.
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.
Department of Biostatistics and Bioinformatics
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
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 12 total credit hours.
Prerequisites: BIOS 500 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.
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
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
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 10 credit hours of required courses and variable credits of optional electives.
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
Course only for BIOS MPH and MSPH students. This course will cover topics dedicated to preparing students for collaborations with non-statisticians in public health and biomedical projects. Covered topics include best practices in data analysis (data inspection, summarization, exploration, visualization, hypothesis formulation, analysis method selection, result interpretation, result presentation etc.), and professionalism as a collaborative statistician. The students will work individually or together in small groups on projects and conduct peer review for each other?s work. In addition, each student will complete a series of milestones for setting up individual capstone/thesis project to be completed in the Spring semester.
Department of Biostatistics and Bioinformatics
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
Spring 2
Includes 2 credit hours of required courses and variable credit hours of electives.
The purpose of the course is to help students with their capstone project in project management, documentation, manuscript writing, and oral/poster presentations while they conduct their independent project with their individual BIOS advisors. Students will learn how to document their research progress, conduct best practice on coding, peer-review each other's work, and write journal articles section by section through lectures and homework assignments. They will develop a manuscript based on their capstone project. At the end of the semester, each student will give an oral presentation on his/her capstone project. Each student will also make a poster on his/her capstone project. Students will receive feedbacks from their peers and instructors to improve their writing and presentation skills.
Department of Biostatistics and Bioinformatics
Master's thesis research.