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## Translational Data Analytics Institute: Brings together Ohio State [faculty](https://tdai.osu.edu/people) and students with industry and community partners to create interdisciplinary, data-intensive solutions to grand challenges.
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We do this by:
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* Supporting interdisciplinary [research communities of practice](https://tdai.osu.edu/research-action/communities-practice) that connect researchers around shared interests and opportunities
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* Providing resources such as [research pilot awards](https://tdai.osu.edu/research-action/tdai-research-pilot-teams), collaborative [work spaces](https://tdai.osu.edu/tdai-pomerene-hall), [trainings](https://tdai.osu.edu/education/training-and-learning-paths), [data science services](https://tdai.osu.edu/resources/data-services) and more
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* Hosting events to foster research ideas and collaborations, such as [forums](https://tdai.osu.edu/events) and [seminars](https://tdai.osu.edu/events/speaker-series)
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* Strengthening community partnerships with a [Master's in Translational Data Analytics](https://tdai.osu.edu/education/masters-translational-data-analytics) for mid-career professionals that responds to industry needs
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* Fostering inclusiveness through a unique [summer camp for middle and high school students](https://tdai.osu.edu/education/data-science-summer-camp) interested in data science and analytics
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* Connecting industry partners with data science and analytics students through innovative internships and events
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* Contributing thought leadership to the development of translational data analytics as a field nationally and around the world
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## Why “translational” data analytics?
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The use of the term “translational” reflects a fundamental shift toward utilizing data science and analytics to solve issues of global importance.
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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.
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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.
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