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Content Analysis of Two-year and Four-year Data Science Programs in the United States

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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

Conference Session

Computing and Information Technology Division Technical Session 1

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Computing and Information Technology

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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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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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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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Data has grown exponentially in the last decade, and this growth has resulted in vast challenges for both business and IT domains (Hassan & Liu, 2019). This growth has given rise to the Data Science field, which has also grown exponentially in the last few years (Hassan & Liu, 2019; Song & Zhu, 2016). The Data Science field has its origins in the statistics and mathematics domain (Cao, 2017b), but is now considered a multidisciplinary field (Aasheim et al., 2015). Data Science warrants knowledge of data analytics, programming, systems, applications, informatics, computing, communication, management, and sociology (Aasheim et al., 2015; Hassan & Liu, 2019; Murillo & Jones, 2019; Cao, 2017a; Tang & Sae-Lim, 2016). The main objective of Data Science is to manage large amounts of complex data and to solve Big Data challenges (Paul & Aithal, 2018) through the implementation of tools, techniques, and visualization strategies (Murillo & Jones, 2019). The rise in the Data Science field has increased the demand for skilled Data Science professionals. Data Scientists collect, prepare, analyze, visualize, manage, and preserve extensive collections of information (Song & Zhu, 2016). To prepare a generation of workers in the skills needed for the Data Science field, higher educational institutions must prepare students to support the Big Data movement and the new technologies developed as a result of this movement (Debnath, 2016; Song & Zhu, 2016). The focus of a Data Science program is to allow students to develop reasoning, analytical, and problem-solving skills needed to gather, process, decipher, and present data in a meaningful way (Debnath, 2016). Many universities are already offering Data Science programs (Song & Zhu, 2016). These programs vary widely in core courses and electives, with some concentrating more on the statistical and mathematical offerings while others on the computer and programming offerings.

The purpose of this study is to conduct a content analysis of 136 two-year and four-year Data Science programs in order to acquire a deeper understanding of the undergraduate data science programs in the U.S. The program profile analysis includes; type of degrees, program names, department/school/college affiliations, type of institutions (private/public), and geographic locations. The study presents a comparative analysis of the accreditation criteria/guideline for Data Science programs established by four accreditation agencies and the results of the evaluation of all 136 Data Science programs for adherence to these criteria. The study provides a roadmap for institutions developing new Data Science programs or updating older programs into Data Science programs. Study findings will inform understanding of the breadth and width of undergraduate Data Science programs in the United States.

Milonas, E., & Li, D., & Zhang, Q. (2021, July), Content Analysis of Two-year and Four-year Data Science Programs in the United States Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36842

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