The Education and Data Science Workforce Group consists of South Hub members from industry, academia, and government actively engaged in securing funding for and developing: (1) the use of government open data to support education and training in data science; (2) faculty and student data science training and curriculum development; (3) broadening and deepening the data science talent and workforce base; (4) connecting training in academia to industry and government needs, and (5) workforce development experiences for students to connect to industry, government, and non-profit organizations.
Chaired by Renata Rawlings-Goss
The South Hub Education and Workforce Working Group is seeking speakers for monthly working group calls. If you are interested in sharing your research, projects, or resources, please contact Kendra Lewis-Strickland and Renata Rawlings-Goss.
The Data Science Education and Workforce Working Group is an open monthly professional working group for data science educators and program leaders to talk and hear from other programs around the country, as well as learn about resources for connecting with data, tools, industry partners, and research.
The focus of the group is to:
- Highlight funded Data Science education projects, programs, and resources
- Share best-practices for project-based courses & teaching approaches
- Provide experiences with assessment or evaluation approaches for Data Science teaching or Data Science programs.
The Education & Workforce Working Group meets virtually the first Friday of every month at 11AM EST, if you are interested in the group, join the mailing list.
Interested in Project-Based Teaching or Program Assessment?
Join of our two sub-working groups focused on these topics. These sub working groups are led by community members to define the direction of the sub-working group and the outputs needed in this community. If you're interested in joining the Project-Based Teaching or Program Assessment sub-working groups, please contact Kendra Lewis-Strickland at firstname.lastname@example.org.
Friday, October 1, 2021 at 11AM EST.
Dr. Da Yan
Talk Title: Some Insights on Curriculum Design in Data Science and Big Data Related Courses
Bio: Dr. Da Yan is an Assistant Professor of Computer Science at the University of Alabama at Birmingham. Dr. Yan is the sole winner of Hong Kong Institute of Science 2015 Young Scientist Award in Physical/Mathematical Science, and senior members of ACM and IEEE. His current research interests include parallel/distributed Big Data systems, graph and geospatial data mining, and deep learning. He regularly publishes in top venues such as SIGMOD, VLDB, KDD, ICDE, WWW, ICML, VLDB Journal, TKDE, TPDS, etc., where he also regularly serves as reviewers. He also organizes the BIOKDD workshop held with KDD and served as guest editors of IEEE TCBB and BMC Bioinformatics. At UAB, he designed and is currently teaching many Data Science courses including Foundations of Data Science, Machine Learning, and Deep Learning, which have been taken by undergraduate, master and PhD students, and even high-school students choosing through UAB’s Vulcan Materials and Academic Success Center. The classes are very popular and taken by students not only within the CS department, but also from many other schools in UAB, with a class size often surpassing 100 students. In this talk, Dr. Yan will share his experience and vision in Data Science and Big Data education.
Dr. Alessandro Selvitella
Talk Title: A journey through data science research at undergraduate-teaching institutions: Thematic programs, conferences, courses, and partnerships towards expanding computational literacy in local communities.
Alessandro is currently an Assistant Professor of Data Science and Applied Statistics in the Department of Mathematical Sciences at Purdue University Fort Wayne. He received a BSc and an MSc in Mathematics from Università degli Studi di Milano (Milan, Italy), a PhD in Mathematical Analysis from SISSA (Trieste, Italy), and an MSc and a PhD in Statistics from McMaster University (Hamilton, Canada). He has been a Postdoctoral Fellow in Applied Mathematics at McMaster University, a Part-time Professor in the Department of Mathematics and Statistics at University of Ottawa, and a Postdoctoral Fellow in the Departments of Psychiatry and Computing Science at the University of Alberta.
His current research interests are in the applications of data science and mathematical methods in the biological and medical sciences. He is currently supported by the Lilly Endowment Grant “Indiana Digital Crossroads” (2021-2026) and the NSF-Simons Pilot Project Program Grant (2021-2022). He has received the PFW Sigma-Xi Researcher of the Year Award (2020), Purdue Service-Learning Fellowship (2021), and Pippert Research Scholar Award (2021).
Working Group Chairs
Renata Rawlings-Goss-Georgia Institute of Technology (chair), Andrew Zieffler- University of Minnesota (co-chair), Chris Tunnell- Rice University (co-chair), and Leah Wasser – University of Colorado Boulder (co-chair).