Public Health Data Science Concentration
What You'll Learn
Data science draws upon multiple disciplines, combining the statistical skills to manipulate data and make inferences, the mathematical skills to model phenomena and make predictions and the computer science skills to manage and analyze large data sets.
Steeped in the public health context, our program offers a unique focus on leveraging the foundational statistical, mathematical and computer science elements of data science to generate useful information from data sources relevant to public health. As a student in this concentration, you will benefit from the instruction and mentorship of top-ranked faculty in the biostatistics department and across the Gillings School. Our chief focus is to optimize data science to help address the most critical public health problems in the world today.
This program will empower you with the knowledge and skills to achieve the following core competencies:
Manipulate data from a variety of sources to support statistical and epidemiological analysis and prepare data summaries.
Select and use data visualization methods to interpret and communicate research results, with the overall objective of conducting reproducible research, both individually and in project teams.
Select and utilize appropriate data analysis and machine learning methods to solve problems and make improvements in a given public health context.
Understand, evaluate and constructively address potential sources of sampling bias and other biases and key sources of uncertainty in data driven health research.
Provide tools that facilitate the expansion of complex statistics and methods to public health contexts traditionally reticent to move away from more traditional approaches, thereby extending the reach of quantitative and methodological innovations in public health.
Required Courses & Sample Plan of Study
In addition to the interdisciplinary, 14-credit Gillings MPH Core, students will take six concentration-specific courses on topics such as experimental analysis, machine learning and epidemiology.
BIOS 512: Data Science Basics
BIOS 645: Principles of Experimental Analysis
BIOS 650: Basic Elements of Probability and Statistical Inference, Part I
BIOS 635: Introduction to Machine Learning
EPID 710: Fundamentals of Epidemiology
BIOS 992: Public Health Data Science MPH Culminating Experience
Public Health Data Science Degree Requirements and Plan of Study (PDF)
A Note About our MPH and MS
The MPH degree is considered a professional degree with the expectation that most students will pursue employment after graduation. The MS degree is considered a research degree in that students are prepared to pursue a doctoral degree after graduation. Note, however, that there can be exceptions to both.
The MPH in Public Health Data Science at UNC is different from our MS program in Biostatistics in several ways:
The MPH student will take a variety of core public health courses in addition to courses in data science, biostatistics, and epidemiology, plus electives. The electives can be taken across campus with the only requirement that they have a data science component and are eligible for graduate course credit. To date, our MPH students have taken electives in the business, economics, computer science, and informatics departments, for a few examples. In contrast, the MS student will focus primarily on biostatistics courses plus electives in public health. The MS curriculum involves more probability and statistical theory and requires a more rigorous mathematical background. As an example, the MPH curriculum includes a 3-credit hour course in probability and statistical inference in the fall and a 3-credit hour course in experimental design and data analysis in the spring. The parallel requirement for MS students requires 6 credit hours each semester, enabling more topics to be covered, and in more detail.
Note that these comparisons are just for the two programs at UNC. Programs at other universities may differ in their requirements, curricula and career opportunities.
Putting research into action
Scientific discoveries made by Gillings School biostatistician Dr. Michael Kosorok (at left) were put into practice by pediatric pulmonologist Dr. George Retsch-Bogart. The result was better hospital care and better health for children with cystic fibrosis.
Hosted by the Gillings School’s internationally renowned Department of Biostatistics, the Master of Public Health (MPH) concentration in Public Health Data Science is designed for students with a strong mathematical background who wish to develop advanced data science skills — including machine learning, data visualization and statistical inference — and apply them in a public health context.
Your education will equip you with the advanced skills needed to:
- Manipulate, mine, search and visualize data from large, complex public health data sets (such as disease registries, population data and health care claims data);
- Apply probability and statistical inference to understand relationships or test hypotheses relevant to health problems and interventions; and
- Contribute data science leadership and expertise to advance practical solutions to public health challenges.
Data science skills are in high demand among employers across a wide array of sectors. Graduates of this concentration may embark on careers as data scientists, data analysts and statistical analysts, among other options.
Career Opportunities
99% of Gillings graduates have a job or continue their education within one year of graduating.
Learn more about the opportunities that await you with an MPH from the Gillings School.
Students fund their education through a combination of sources, including loans, fellowships and awards, assistantships and grants. Several awards and scholarships are supported by generous contributions to the School and University who value the current and future contributions of our students.
Contact: Sarah Kitchens, Funding and Awards Coordinator, sarah_kitchens@unc.edu
