Applications are now open for Faculty Data Science Fellowships offered by the UO’s Social Systems and Data Science Network to any faculty member interested in applying data science methods to their research.
The application can be found online and will be due in mid-November. UO faculty members should choose the “Interested in Becoming a Member” option when asked which level of network membership they are applying for.
Accepted faculty members will join a cohort of approximately 20 peers in virtual workshops building a greater knowledge of data science research methods. The workshops will continue through the 2021 academic year, culminating in a final project with learned techniques applied to their own research.
“Faculty fellows will gain a simple but powerful understanding of the latest data science methods in social systems,” said Kathleen Scalise, co-convener of the network. “They will receive accessible instruction, extensive coaching and hands-on guidance in their fields of interest.”
The curriculum will focus on data science techniques like machine learning, text mining, advanced classification and neural networks. Many of those techniques require the knowledge of Python, the current leading programming software used in data science.
Data science uses quantitative and analytical approaches to extract insights from large, dense data sets. The goal of the Social Systems Data Science Network and its Faculty Data Science Fellowship program is to encourage further use of data science across a wider range of disciplines.
“Given that big data are increasingly common in educational and social systems research, the time was ripe to convene the SDS network as part of the larger UO Data Science Initiative, underscoring and elevating UO’s place as a leader in social aspects of data science applications,” said Emily Tanner-Smith, co-convener of the network.
Core members of the network have been conducting data science research since the program’s founding in 2019. For example, research associate professor Sung-Woo Cho presented his research on student success to the UO Board of Trustees on Sept. 10. Cho also contributed to COVID-19 research by analyzing 50,000 COVID-related journal articles in two weeks using data science techniques.
“The goal is to use machine learning to try and get a handle on what the main themes and topics are in large bodies of published research in a more efficient manner,” Cho said.
Other core faculty members research mental health, general well-being, substance abuse, early intervention programs, special education and more using data science methods.
The network will also host a guest speaker this fall. Min Sun, an associate professor at the University of Washington, will present her work on machine learning in education research Nov. 20, at a time to be announced.