$99,760 Grant to Advance Study of Data Science at ATU

ATU Data Science Grant Project Research Committee Fall 2021
Arkansas Tech University faculty members (photographed from left-to-right) Dr. Weijia Jia, Dr. Xinli Xiao, Dr. Jacob Grosskopf, Dr. Christopher Kellner, Dr. Matthew Wilson and Dr. Wan Wei comprise a grant project research committee that is forwarding the study of data science at ATU.

The study of data science at Arkansas Tech University will soon be enhanced by a two-year, $99,760 Supporting Effective Educator Development (SEED) grant from the Arkansas National Science Foundation Established Program to Stimulate Competitive Research (NSF EPSCoR).

Dr. Weijia Jia, assistant professor of statistics at ATU and director of the university’s applied statistics degree program, is providing leadership for the grant project.

“The objective of this research is to collect series of datasets from different disciplines related to the research of faculty members at Arkansas Tech University and preparing data science course projects, practicum courses, capstone projects and senior design projects for college-level data science education,” said Jia. “The projects and designs would open the door of the data life cycle to the students in data science-related classes, instill curiosity about the data, motivate the students and better prepare them to enter the workforce.”

Jia is joined on the grant project research committee by ATU faculty colleagues Dr. Jacob Grosskopf, assistant professor of geology; Dr. Christopher Kellner, professor of wildlife science; Dr. Matthew Wilson, assistant professor of agriculture; Dr. Wan Wei, assistant professor of economics; and Dr. Xinli Xiao, assistant professor of mathematics.

The grant is part of the Arkansas NSF EPSCoR Data Analytics that are Robust and Trusted (DART) program. According to the Arkansas Economic Development Commission (AEDC), DART seeks to “address fundamental barriers to practical application and acceptance of modern data analytics” and overcome any barriers that could “derail or impede its full development and contributions.”

Potential barriers to the application and acceptance of modern data analytics defined by AEDC are big data management, security and privacy and model interpretability.

“The data science team of this research also plans to recruit and lead undergraduate students at ATU to participate in data visualization, wrangling and pre-processing, which provide students a starting point of understanding the vitality, diversity and complexity of data,” said Jia. “This project will benefit the data science program course designs by easing the burden of the course project preparation and supporting development of data science education at different educational institutions in Arkansas.”

Visit www.atu.edu/mathematics/sturln-appliedstatistics.php to learn more about the study of data science at Arkansas Tech.