From scientific and mathematical discoveries through computation and pattern recognition, to the visualization and analysis of demographic and linguistic data, and the analysis of texts and images, the continuing expansion of digital data and our ability to store, retrieve, and analyze it is bringing about an epistemological revolution. We are producing knowledge in new ways an at an ever-increasing pace. Empirical investigation has replaced theoretical speculation in areas as diverse as systems biology and the micro-foundations of macroeconomic phenomena. It is also a pragmatic revolution. Data science now permeates education, government, medicine, engineering, entertainment, science, the arts, humanities, and business, touching nearly every facet of life. In the university, we are enabling education and investigation to dig deeper and scan a broader phenomenal horizon. This infusion of data has led to a pressing need to teach students data-related skills, knowledge, ethics, and literacy, and to support faculty and staff in the collection, stewarding, retrieval, and analysis of data, whether in educational programs, research initiatives, or operations.
Many academic units at the University of Pittsburgh recognize this need and are responding individually with initiatives around data science. We have a burgeoning variety of “analytics,” “digital,” “computational,” “informatics,” and “-omics,” in fields as far flung as business, health sciences, social sciences, law, and humanities, as well as adaptive learning and student advising platforms and practices. Coordinating and collaborating on these activities could leverage our collective ability, jointly build capacity to meet the demand, and foster opportunities for new interdisciplinary educational and research programs. Other universities are undertaking similar coordination through a range of approaches, such as independent institutes; schools of data science; virtual divisions of data-oriented academic units; research and learning hubs in data science; data spaces and services in libraries; computing for machine learning, visualization, and data management; and interdisciplinary data science degrees. Pitt has enormous opportunities to create an exciting new initiative to bring all of this together with the recently launched the School of Computing and Information, and the plans for a new building to house the School and data science-related research and educational activities.
How should the broader Pitt academic community collectively act on the urgent need, given our context, strengths, and individual efforts in data-related areas? To this end, the Provost charges the Ad Hoc Committee on Data Science with recommending a coordinated strategy to catalyze, nourish, and sustain educational programs and research initiatives that (1) equip undergraduate and graduate students with the knowledge and skills necessary for the increasingly data-oriented world; (2) develop and use data science methods in research; and (3) attract and retain faculty using data and associated methods in their disciplines. In doing so, please answer these questions and any others that are deemed relevant by the committee:
- What current and future data-related educational and research programs exist at Pitt, and which would benefit from a coordinated strategy? How could new opportunities be enabled?
- What approaches would facilitate leveraging and interfacing current and future initiatives to ensure proficiency in data-related areas by our students, and to support and accelerate research relying on data science?
- What nomenclature and standards should be adopted to enable a shared foundation of education and research capacity in data science by and for the Pitt community?
- How can the identified approaches be implemented? How should the approaches be prioritized, and what should be done in the short, medium, and long terms?
In answering the questions, please consult the Pitt community broadly to seek their input. Please establish an inclusive process for this engagement at the start of the committee’s work.
By January 30, 2020, please submit an interim report that summarizes the committee’s findings to that date, with the complete report due by June 30, 2020.