Data science is an ever-evolving field. It continues to change, as new innovations come to light, and data scientists continue to revolutionize the way we use, analyze, collect and store data.
So, what is the next big thing for data science?
The Data Sciences Institute (DSI) is launching a new Emergent Data Sciences Program competition designed to fund researchers and advance cross-disciplinary data science in areas where the University of Toronto is already a leader or has the capacity to become one. The DSI seeks to promote and expand the awareness and role of data science in all research activities across UofT. This call is intended to attract and develop new communities that want to enhance their activities using data science.
Emergent Data Sciences Programs are a DSI core activity that helps fulfil its mission of bringing people together for collaborative generation and application of new ideas that support emergent areas in the data sciences.
“At the DSI, we are proud to be a part of the University of Toronto, one of the world’s leading universities. I have no doubt that we will see many proposals that attempt to push the boundary of what has been done before from across UofT, as well as explore new multi-disciplinary applications of data science. We hope these will establish new fields and techniques that are so often the preludes to future scientific, technological and societal breakthroughs,” says David Lie, DSI Associate Director, Thematic Programming and Data Access.
When asked about what he thinks the next big thing in data science is Dr. Lie says, “The collection, application, and curation of large datasets focused on the public has been largely spearheaded by private entities trying to improve their enterprises and businesses. However, the Covid-19 pandemic demonstrated that there are a host of socially beneficial uses of that data. This is just the tip of the iceberg. I believe the next stage of data science will be to devise new techniques, and governance policies, that will enable data collected by private and public organizations to be shared and applied in other, important socially beneficial uses. To do this, we must overcome significant challenges, such as how we can share large data sets in privacy-preserving ways, and how we can identify and mitigate security risks that might arise. But this is just one example of many.”
Emergent Data Sciences Program proposals should include a broad span of activities that lead to the development of innovative data science methodologies, deep connections with computation and applied disciplines, new training programs, collaboration, knowledge mobilization, and impact. Ideal program proposals should establish or elevate local cross-disciplinary activity that advances the data sciences by pursuing the next big-but-yet-unknown data-driven field or computational or analytic breakthrough.