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.
Friday, August 6, 2021 at 11AM EST.
Dr. Jian Tao
Talk Title: DS+X: an Immersive and Interdisciplinary Approach for Data Science Education
Bio: Dr. Jian Tao is a Research Scientist / Computational Scientist / Adjunct Professor at Texas A&M Engineering. He is affiliated with the Texas A&M High Performance Research Computing, the Texas A&M Institute of Data Science, and the Department of Electrical & Computer Engineering at Texas A&M University. He received his Ph.D. in Physics (Computational Astrophysics) from Washington University in St. Louis in 2008. He is currently the Associate Director of the Scientific Machine Learning Laboratory at the Texas A&M Institute of Data Science. He is a contributor to the SPEC CPU 2017 benchmark suite. He is also a University Ambassador of the NVIDIA Deep Learning Institute and an XSEDE Campus Champion at Texas A&M University. His research interests include high performance computing, numerical algorithms, image processing, data analytics, machine learning, and workflow management.
Dr. Arko Barman
Talk Title: Case study: Applied Data Science & Machine Learning Capstone designed for externally-funded projects
Bio: Dr. Arko Barman is an Assistant Teaching Professor at the Data to Knowledge Lab with a joint appointment in the ECE department at Rice University. Dr. Barman has been involved in curriculum design and teaching data science courses for several years. Prior to joining Rice University, Dr. Barman was a postdoctoral research fellow at the University of Texas Health Science Center (UTHealth), where he developed introductory data science and statistics courses for postdoctoral fellows in biology and medicine. He received the Excellence in Teaching Award and the inaugural Postdoctoral Service Award at UTHealth for his teaching and service activities respectively. Dr. Barman received his Ph.D in Computer Science from the University of Houston, Masters in Signal Processing from the Indian Institute of Science, and his Bachelors in Electrical Engineering from Jadavpur University, India. His research interests include machine learning, deep learning, biomedical image analysis, computer vision, data mining, and heuristic optimization. Dr. Barman has also worked in the industry for several years at Broadcom Corporation and the Palo Alto Research Center (Xerox PARC).
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).