A groundbreaking new collaboration between telecommunications giant AT&T and SMU will deliver high-level training, practical experience and a potential employment offer in the field of data science for a group of University students spending the summer together in the classroom and on the job.
AT&T is covering the cost of the training for the students and for the overlapping on-site internship. After the program ends, each participant who earns an SMU certificate for completing the on-campus boot camp and the internship will receive interviews for permanent positions with AT&T after graduation.
“We’ve had interns for years, but we’ve never really done a boot camp where we actually have the formal training using the Artificial Intelligence tools we use here internally at AT&T and then collaborate on projects, too,” says Mark Austin, AT&T’s vice president for data science. “So, this is unique, and we’re excited about it.”
The nine students selected for the program are spending half of the summer in an SMU classroom led by Bivin Sadler, technical assistant professor and course lead faculty for SMU’s online Master of Science in Data Science program. Part of that “boot camp” experience includes a competition between the students, divided into teams, working to solve problems presented by their AT&T mentors. Following the SMU instruction, the group will head to AT&T offices for the second half of the summer to work with the massive data sets and corporate-level challenges that are bread-and-butter to the communication company’s own data science group.
The Data Science Scholars are a mix of undergraduate and graduate students pursuing degrees in various STEM fields – data science, statistics, math and engineering.
Demand for data scientists is expected to increase by 22 percent over the next decade, according to estimates by the U.S Bureau of Labor Statistics. However, Black and Hispanic workers remain underrepresented in the STEM workforce. Women, who now earn the majority of undergraduate and advanced degrees, are significantly underrepresented in computer science fields.