Ohio State Translational Data Analytics Institute brings together university faculty and students with industry and community partners to create data science and analytics solutions to grand challenges for the greater good. We do this by:
- Advancing research agendas around specific topics and societal challenges through interdisciplinary communities of practice and team science, with support to accelerate industry-sponsored projects and tech transfer
- Providing essential resources such as collaborative work space and labs; seed grants for research; proposal development support; and shared services consultations on data science and analytics services, training, and resources throughout the university
- Hosting forums, seminars and other events that foster new research collaborations and the exchange of ideas
- Connecting industry partners with talented students utilizing data science and analytics in roughly 30 different majors and minors, through innovative internships, events, challenges and research projects
- Creating a Professional Science Master’s degree in translational data analytics that is responsive to industry needs, student tools such as a program and course directory and job-search materials, and a summer camp for high school-age girls interested in data science and analytics
- Contributing nationally and internationally to advancing translational data analytics as a field
From data science and analytics foundations to domain-specific applications, TDAI leverages Ohio State's breadth of expertise to answer real-world needs across sectors, including health care, agriculture and the environment, business, law and policy, and more.
Why “translational” data analytics?
The use of the term “translational” reflects a fundamental shift toward utilizing data science and analytics in solving issues of global importance. In 2014, TDAI defined “translational data analytics” as the application of data analytics theories and methods to generate solutions for real world problems, or use cases, derived from consultation with impacted stakeholders, and the subsequent delivery and dissemination of those solutions in a manner that enables stakeholders to use them in a tangible and quantifiable way. The National Science Foundation later applied the “translational” concept to data science: “Translational data science” is a new term that is being used for an emerging field that applies data science principles, techniques, and technologies to challenging scientific problems that hold the promise of having an important impact on human or societal welfare. The term is also used when data science principles, techniques and technologies are applied to problems in different domains in general, including—but not restricted to—science and engineering research.
"Translational data analytics" is translating data into knowledge and action to benefit the world.
—TDAI Faculty Director Dr. Tanya Berger-Wolf