The accurate interpretation and use of data is crucial to understanding health needs and devising and implementing comprehensive, evidence-based solutions.

Our Public Health Data Science concentration — one of the first applied data science programs situated within a school of public health — will give you the skills and knowledge to employ cutting-edge data science tools and, in turn, advance effective solutions to pressing public health issues.

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.

Career Opportunities

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.

Learn more about the opportunities that await you with an MPH from the Gillings School.

Required Courses

In addition to the interdisciplinary, 15-credit Gillings MPH Core, students will take:

  • 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

Concentration Competencies

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.

Requirements and Sample Plan of Study

Public Health Data Science Degree Requirements and Plan of Study (PDF)

Ready to Apply? Visit our Apply page for more information.


Want to learn more?

Adia Ware, Lead Academic Coordinator
202A Rosenau Hall

Concentration Leader

Lisa LaVange, PhD
Professor and Associate Chair
Department of Biostatistics