July 26, 2021
July 26, 2021
July 19, 2022
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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