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1. Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science

   National Academies of Sciences, Engineering (January 2021, Publications listing National Academy of Sciences National Academy of Engineering Institute of Medicine National Research Council)

   null (Ed.)

   The effective use of data science - the science and technology of extracting value from data - improves, enhances, and strengthens acquisition decision-making and outcomes. Using data science to support decision making is not new to the defense acquisition community; its use by the acquisition workforce has enabled acquisition and thus defense successes for decades. Still, more consistent and expanded application of data science will continue improving acquisition outcomes, and doing so requires coordinated efforts across the defense acquisition system and its related communities and stakeholders. Central to that effort is the development, growth, and sustainment of data science capabilities across the acquisition workforce. At the request of the Under Secretary of Defense for Acquisition and Sustainment, Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science assesses how data science can improve acquisition processes and develops a framework for training and educating the defense acquisition workforce to better exploit the application of data science. This report identifies opportunities where data science can improve acquisition processes, the relevant data science skills and capabilities necessary for the acquisition workforce, and relevant models of data science training and education. 

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2. ADOPTING A FOUNDATIONAL DATA SCIENCE COURSE: A FOLLOW-UP EVALUATION

   https://doi.org/10.21125/edulearn.2025.1949  

   Wang, Susan; Harding, David; von_Vacano, Claudia (June 2025, IATED Digital Library)

   na (Ed.)

   To broaden participation and diversity in data science, educators are increasingly leveraging the adaptation and sharing of successful course models. This paper presents our experience implementing a foundational data science course, adapted from the University of California, Berkeley's Data 8 Foundations of Data Science, at Northeastern University's Oakland Campus in Spring 2024. A key objective was to cultivate student engagement and demonstrate the relevance of data science across disciplines. We assessed the impact of this adaptation on a cohort of first-year students, all non-data-science majors with limited prior programming or statistical experience. Our evaluation focused on student engagement, academic trajectory, and the course's ability to spark sustained interest in data science. The results demonstrate a significant positive impact: 44% of students declared a major in data science or a combined major (e.g., data science and business or economics), 16% pursued a minor in data science, and 16% transitioned to computer science. These outcomes emphasize the importance of designing introductory data science curricula to serve diverse student populations. By incorporating real-world applications from health, economics, social sciences, entertainment, sports, and finance, students gained a deeper understanding of the field's potential and their own capacity to contribute. Furthermore, smaller class sizes promoted interactive learning and personalized assignments, creating a more engaging and accessible educational experience. This approach effectively strengthens students' comprehension of data science pathways and cultivates motivation, ultimately contributing to a more inclusive and diverse data science workforce. 

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3. Establishing ABET Accreditation Criteria for Data Science

   https://doi.org/10.1145/3408877.3432445  

   Blair, Jean R.; Jones, Lawrence; Leidig, Paul; Murray, Scott; Raj, Rajendra K.; Romanowski, Carol J. (March 2021, 52nd ACM Technical Symposium on Computer ScienceEducation(SIGCSE’21),)

   null (Ed.)

   Prompted by the skyrocketing demand for data scientists, progress made by the ACM Data Science Task Force on defining data science competencies, and inquiries about data science accreditation, ABET is in the process of developing accreditation criteria for undergraduate data science programs. The effort is led by members of a joint data science criteria subcommittee appointed by ABET’s Computing Accreditation Commission (CAC) and CSAB (the lead society for computing accreditation). Establishing data science accreditation criteria is a notable milestone in the maturing data science discipline, indicating the presence of an accepted body of knowledge, standards of practice, and ethical codes for practitioners. This position paper motivates the effort and discusses prior work towards defining data science education requirements. It describes the ongoing process for creating and obtaining approval of the accreditation criteria, and how feedback was and will be solicited from the computing and statistical communities. The current draft data science criteria, which was approved in July 2020 by the relevant ABET bodies for a year of public review and comment, is presented. These criteria emphasize the three pillars of data science: computing foundations, mathematical/statistical foundations, and experience in at least one data application domain. This report thus serves both to inform and to stimulate the academic discussion needed to finalize appropriate data science accreditation by ABET. 

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4. Keeping Data Science Broad: Negotiating the Digital and Data Divide Among Higher-Education Institutions

   Rawlings-Goss, Renata; Cassel, Lillian; Cragin, Melissa; Cramer, Catherine; Dingle, Angela; Friday-Stroud, Shawnta; Herron, Al; Horton, Nicholas; Inniss, Tasha R; Jordan, Kari; et al (January 2018, Workshop: Bridging the Digital and Data Divide)

   A report summarizing the “Keeping Data Science Broad” series including data science challenges, visions for the future, and community asks. The goal of the Keeping Data Science Broad series was to garner community input into pathways for keeping data science education broadly inclusive across sectors, institutions, and populations. Input was collected from a community input survey, three webinars (Data Science in the Traditional Context, Alternative Avenues for Development of Data Science Education Capacity, and Big Picture for a Big Data Science Education Network available to view through the South Big Data Hub YouTube channel) and an interactive workshop (Negotiating the Digital and Data Divide). Through these venues, we explore the future of data science education and workforce at institutions of higher learning that are primarily teaching-focused. The workshop included representatives from sixty data science programs across the nation, either traditional or alternative, and from a range of institution types including community colleges, Historically Black Colleges and Universities (HBCU’s), Hispanic-Serving Institutions (HSI’s), other minority-led and minority-serving institutions, liberal arts colleges, tribal colleges, universities, and industry partners. 

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5. A Cross-disciplinary Review of Introductory Undergraduate Data Science Course Content

   https://doi.org/10.1145/3626253.3635352  

   Posner, Michael A; Kerby-Helm, April; Unfried, Alana; Whitaker, Douglas; Bond, Marjorie E; Batakci, Leyla (March 2024, ACM)

   Data Science is one of the fastest growing fields with unmet demand from employers. Many academic institutions have taken on the task of creating programs to meet both current and future needs and demands. Data science, as a field, integrates aspects of computer science, statistics, and subject matter expertise which encourages cross-disciplinary conversations and collaboration. In this talk, we present results from a broad survey of instructors of introductory college-level data science courses for undergraduates. In addition, we explore the alignment of these findings with the recommendations of various professional organizations. We conducted a national survey on topics covered in introductory, college-level data science courses. With responses from computer scientists, statisticians, and allied fields, these results represent a wide array of instructors of data science. The survey identifies topics commonly covered, the amount of time spent on each, common and divergent definitions of data science, and course materials used. These results will be presented. We will then discuss the alignment of these results through a rigorous review and synthesis of recommendations from various professional organizations. These include Association for Computing Machinery's Computing Competencies for Undergraduate Data Science Curricula[1], the National Academies of Science, Engineering, and Medicine’s Data Science for Undergraduates: Opportunities and Options[2], the Park City Math Institute's report Curriculum Guidelines for Undergraduate Programs in Data Science[3], and the American Statistical Association’s Two-Year College Data Science Summit Final Report[4] and Curriculum Guidelines for Undergraduate Programs in Statistical Science[5]. We will also explore alignment with ABET’s accreditation of data science.[6] 

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