Goals for RDS@Pitt
In its 2019 report, the Data Science Task Force recommended a framework of goals and actions to coordinate and grow data science across campus into an institutional focal point. The framework was based on the task force's observations, background study, and the committee’s expertise, knowledge, and experiences. The goals were designed to provide direction, and the actions laid out tangible and specific steps that can be undertaken, building toward the opportunity.
With the development of RDS@Pitt, three of these goals have been updated to refine the framework.
- Updated Goal 1: Create shared understanding of how to translate data science into impact across domains.
- How do we teach, demonstrate, learn, and discover best practices within and across domains?
- See What Is Responsible Data Science and Welcome from the Associate Vice Provost for Data Science.
- Updated Goal 2: Catalyze and incentivize the acquisition of fluency and knowledge of responsible data science for every student and trainee.
- How do we provide in-class and outside-of-class opportunities for everyone across Pitt to enrich responsible data science?
- See Education and Training.
- Updated Goal 3: Coordinate strategy and action in responsible data across Pitt.
- How do we institutionalize cross-unit collaboration and the sharing of best practices in applied data science?
- See RDS@Pitt Organization and Connect with RDS@Pitt.
Vision for RDS@Pitt
The preeminent institution for research and training that empowers people to appropriately engineer and steer data-powered resources to make responsible, impactful decisions as the technology of data science continuously evolves. The power of data science is in its uses and the decisions that it influences.
Mission of RDS@Pitt
Empower responsible, use-driven data science across the breadth of the university to solve societal challenges.
Points of focus:
- Responsible use
- Applications and foundations
- Diverse and inclusive
“[D]ata science’s true spirit [is] to provide insight about the world around us while centering ethical and responsible services, outcomes, and technologies.” — From Data Science Ethos Project (2023), identifying a gap in training and conceptualization of data science thus far.