Publications and Research

Document Type


Publication Date



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. © 2021 American Society for Engineering Education.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.