Data Science

UMass Dartmouth’s Data Science program offers an interdisciplinary approach to this emergent field, building on the university’s centers of excellence in engineering, computer science, mathematics, statistics and an array of academic disciplines.

The power of big data

The program encourages students to harness the power of big data to transform fields such as business, government, healthcare, and the sciences. Our curriculum integrates computer and information science, mathematics and statistics, and discipline-specific studies.

Undergraduate and graduate students focus on data-intensive methodologies and applications, applying their knowledge in both research and real-world settings. 

Student outcomes

Through course work that includes information theory, database design, and data visualization, students will:

  • Build a strong foundation in data-driven discovery and complex problem solving
  • Develop and use automated methods to collect and analyze data to extract knowledge
  • Gain new insights for problem-solving and decision support, encompassing informatics, data analysis, and predictive and visual analytics
  • Foster collaborations for the use of data within a variety of fields such as business, finance, government, healthcare, science, social networking, and telecommunications
  • Collect and manage big data, extracting value and competitive intelligence for industries, organizations, and agencies
  • Apply skills and demonstrate knowledge through opportunities in industry, agency, and faculty-sponsored research projects
Unique attributes
  • UMass Dartmouth’s College of Engineering ranks #47 in the nation for undergraduate engineering programs.
  • Faculty research centered on data science across the disciplines: to treat disease, monitor neonate health, explore the universe, predict ocean turbulence, and solve complex numerical equations.
  • Research opportunities for both undergraduate and graduate students to pursue their own interests and collaborate on industry, agency and faculty-sponsored research projects.
  • University Studies curriculum that complements the interdisciplinary nature of data science, with an emphasis on integrating concepts across disciplines, communicating ideas effectively, and applying knowledge through action.
March 2015

Collaborative research

Center for Scientific Computing and Visualization Research: An interdisciplinary center where faculty and students collaborate using high-performance computers to address mathematical, engineering, physics, biology, chemistry, and oceanography computational issues and questions.
Learn more about CSCVR >