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Content Analysis of Data Science Graduate Programs in the U.S.

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2021 ASEE Virtual Annual Conference Content Access


Virtual Conference

Publication Date

July 26, 2021

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July 26, 2021

End Date

July 19, 2022

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Graduate Studies Division Technical Session 4

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Graduate Studies

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Duo Li Shenyang City University Orcid 16x16

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Dr. Duo Li is the chief research scientist of Green Island Hotel Industry Research Institute of Shenyang City University. Duo Li is the member of ASIST&T and his research interests are focusing on Human-Computer-Interaction, Big Data, Data Analytics, Social Networking, and Hospitality Management.

Skilled professional experienced in big data, data analysis, bibliometric, social networking sites, statistic software, and online learning system. Full skilled in establishing, operating, and maintaining online course on Blackboard. Educated in data visualization, multidimensional scaling analysis, and human computer interaction. Well versed in Camtasia, and graphics processing software.

Doctor of Philosophy in Information Studies, May 2017. LONG ISLAND UNIVERSITY, POST CAMPUS, Brookville, NY

Master of Science, Management Engineering, January 2010. LONG ISLAND UNIVERSITY, POST CAMPUS, Brookville, NY

Bachelor of Science, Automotive Engineering, July 2007. BEIJING INSTITUTE OF TECHNOLOGY, Beijing, P.R. China

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Elizabeth Milonas New York City College of Technology

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Elizabeth Milonas is an Assistant Professor with the Department of Computer Systems at New York City College of Technology -City University of New York (CUNY). She currently teaches relational and non-relational database theory and practice and Data Science courses to undergraduates in the Computer Systems Major. Her research focuses on three key computer areas: Web: research on the mechanisms used to organize big data in search result pages of major search engines, Ethics: techniques for incorporating ethics in computer curriculum specifically in data science curriculum and programs/curricula: evaluating Data Science programs in the US and China.

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Qiping Zhang Long Island University Orcid 16x16

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Dr. Qiping Zhang is an Associate Professor in the Palmer School of Library and Information Science at the C.W. Post Campus of Long Island University, where she also serves as director of the Usability Lab. Dr. Zhang holds a Ph.D. and an M.S. in information and library studies from the University of Michigan, Ann Arbor, and an M.S. and a B.S. in cognitive psychology from Peking University in Beijing, China. Prior to joining Long Island University in 2006, she worked at Drexel University, IBM Waterson Research Center, and Institute of Psychology at Chinese Academy of Science.

Dr. Zhang's general research areas are human-computer interaction (HCI), knowledge management (KM), social informatics and distance learning. Her primary interests lie in the areas of computer-supported cooperative work (CSCW) and computer-mediated communication. Specifically, she is interested in facilitating productive collaborations of individuals who are geographically and culturally distributed. Dr. Zhang has published numerous papers in the areas of HCI, CSCW, KM, social informatics and related disciplines.

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Content Analysis of Data Science Graduate Programs in the U.S. Duo Li, Elizabeth Milonas, Qiping Zhang

Abstract Data science is an emerging academic field (Paul & Aithal, 2018), which has its origins in “Big Data/Cloud Computing” and complexity science domains. Data Science is about managing large and complex data (Big Data management) and analytics technologies (Paul & Aithal, 2018). Data, technology, and people are the three pillars of data science. In addition, Data Science is composed of three key areas: analytics, infrastructure, and data curation (Tang & Sae-Lim, 2016). Stanton (2012) defined data science as “an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information (Song & Zhu, 2016). Data science programs emphasize the implementation of tools, techniques, and visualization strategies, while data analytics programs emphasize the use of case studies and evolutions of tools (Murillo & Jones, 2019). Data science experts are needed in virtually every job sector, not just in technology. KDnuggest, a leading website on Big Data (Miller, 2020) reports that “Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.” In a study conducted by Bukhari (2020), a content analysis of the 30 Master’s Degree curricula in Data Science, revealed that schools that offer these programs are diverse: business, computer science, and science schools. On an average, Data Science master's programs required 18.3 credits and 9.7 courses to complete the core requirements. However, there were inconsistencies in terms of the requirement across the 30 programs reviewed in this study. The objective of the study is to survey U.S. graduate programs in data science to understand the current situation of data science graduate education in the U.S.. The comparison of such program analyses with corresponding accreditation criteria will allow us to understand the stage of these programs, whether they are still in infancy or if they are on the path to maturity. A total of 422 graduate data science programs are analyzed in terms of their program profiles, including the degree names, department/school affiliation, geographic locations, types of universities (private vs. public). In addition, accreditation/guideline data from four accreditation agencies for graduate data science programs are analyzed. Corresponding accreditation analysis with all 422 programs will be reported. There are two major Implications from this study. On the one hand, findings from this study will provide an overview as well as a reference for any high education institutions to develop their own graduate data science program. On the other hand, practitioners in various industry or government segments will better understand the working force applying for Data Science jobs. It will start a dialogue between academia and industry partners to better prepare the Data Science work force.

Li, D., & Milonas, E., & Zhang, Q. (2021, July), Content Analysis of Data Science Graduate Programs in the U.S. Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36841

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