data science

SPH Introduces MS in Public Health Data Science

The new specialized degree is designed to prepare the next generation of public health researchers to develop data-driven solutions for tackling some of the most pressing health challenges of our time.

The School of Public Health is at the forefront of innovative public health research and education, preparing students to work on the frontlines of a rapidly evolving domestic and global landscape.

Now, with employment in the field of data science projected to grow by an estimated 11.5 million jobs globally by the year 2026, according to the U.S. Bureau of Labor Statistics, SPH has launched a Master of Science in Public Health Data Science degree under the MS in Population Health Research umbrella program.  

The new specialized degree is designed to prepare the next generation of public health researchers to develop data-driven solutions for tackling some of the most pressing health challenges of our time.

“Opportunities in public health data science are expanding rapidly,” says Andrew Stokes, director of the MS in Population Health Research degree program and assistant professor of global health. “This is an exciting opportunity for our students to gain the skills and knowledge needed to leverage tools like machine learning, algorithms, and artificial intelligence to advance public health research and innovation.”

By combining empirical methods from the fields of biostatistics and epidemiology with tools from computer science and theoretical models from social sciences like sociology, demography, and economics, data science takes an interdisciplinary approach to offer real-time insights into population-level problems through non-traditional datasets. Instead of using survey data that may be two or more years old, data science tools gather timely information to inform a targeted public health response.

Several SPH researchers regularly use these novel methods to support their public health work.

Jonathan Jay, assistant professor of community health sciences, largely uses data science tools to better understand how cities work in order to prevent injuries. One project that he leads, called Shape-Up, uses machine learning to incorporate photographs as data in analyses of the built environment to understand where gun violence is likely to occur. Evidence gathered from this study can be used to help cities better understand where they should focus their efforts to prevent violence by fixing up neighborhood spaces.

In the early months of the COVID-19 pandemic, Jay also worked with colleagues at SPH to gather daily data from millions of smartphones across the country to see how population-level mobility changed with stay-at-home orders and how economic inequities influenced who could actually stay home.

“Using data science gave us an incredible volume of timely, real-world observations that had enormous advantages compared to more traditional methods of figuring out how people had moved around,” he says.

Prasad Patil, assistant professor of biostatistics, also uses machine learning applications to address a variety of public health concerns. Much of his work is focused on forecasting and risk prediction using multiple, often disparate, data settings, including genomics, air pollution monitoring, and opioid surveillance using administrative claims.

“Data science is integral to modern public health research because health data are being collected from a myriad of sources in large quantities and at incredible rates,” says Patil. “Many of these are non-experimental data and require expertise merely to prepare them for a meaningful analysis, and that is where a public health data scientist can step in and extract novel and actionable insights to drive change.”

Throughout the accelerated, one-year program, students can expect to gain invaluable skills in data management and analysis to inform evidence-based policy, as well as in applying state-of-the-art methods to enhance research study design and data collection strategies.

A unique component of the program is the 400 hours of research that students will complete with a faculty member.

Tianchu Hang, a current student in the public health data science program, says that the mentored research experience has been a highlight of her time at SPH so far.

“I have been able to perfectly put into practice what I have learned in the classroom,” she says. “The hands-on skills students will gain through the research experience will prepare them well for work and continued study long after SPH.”

Throughout their time in the program, students will also have the opportunity to connect with and work alongside faculty members outside of SPH at any of the Boston University centers conducting data science work, including the Center for Computing & Data Sciences, the Rafik B. Hariri Institute for Computing and Computational Science & Engineering, the Center for Antiracist Research, and the Center for Emerging Infectious Diseases Policy & Research.

“Data science is an exciting field because there are so many opportunities to use these methods to solve public health problems, from studying food safety and neighborhood obesity prevalence, to miscarriages and infectious disease forecasting,” says Elaine Nsoesie, assistant professor of global health. “No matter where the field takes them, the skills that our students will gain throughout this program will always translate and be in high demand.”

Learn more about applying to the MS in Public Health Data Science program here. Under the MS in Population Health Research umbrella program, SPH also offers degrees in Climate and Health, Epidemiology, and Global Health, along with a customizable degree option.