Computing Competencies for Undergraduate Data Science Programs

Danyluk, Andrea; Leidig, Paul M.; McGettrick, Andrew; Cassel, Lillian N.; Doyle, Maureen; Servin, Christian; Schmitt, Karl; Stefik, Andreas

doi:10.1145/3408877.3432586

Cited by 23 publications

(35 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this in mind, it is important to raise awareness of Fairness, Accountability, Transparency and Ethics (FATE) issues in a higher education context. There have been increasing calls for these aspects to be included in the curriculum and training of data scientists in general [Danyluk et al, 2021]. However, as studies (e.g., [Bates et al, 2020]) have shown, this comes with its own challenges.…”

Section: Introductionmentioning

confidence: 99%

Report on the CyCAT winter school on fairness, accountability, transparency and ethics (FATE) in AI

et al. 2021

View full text Add to dashboard Cite

The first FATE Winter School, organized by the Cyprus Center for Algorithmic Transparency (CyCAT) provided a forum for both students as well as senior researchers to examine the complex topic of Fairness, Accountability, Transparency and Ethics (FATE). Through a program that included two invited keynotes, as well as sessions led by CyCAT partners across Europe and Israel, participants were exposed to a range of approaches on FATE, in a holistic manner. During the Winter School, the team also organized a hands-on activity to evaluate a tool-based intervention where participants interacted with eight prototypes of bias-aware search engines. Finally, participants were invited to join one of four collaborative projects coordinated by CyCAT, thus furthering common understanding and interdisciplinary collaboration on this emerging topic.

show abstract

Section: Introductionmentioning

confidence: 99%

Report on the CyCAT winter school on fairness, accountability, transparency and ethics (FATE) in AI

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Table 3 below displays the states in each region. 5 presents the competencies used in this research, the corresponding competencies presented in the 2021 ACM Task Force Report [17], and sample courses for each competency. • This bottom up approach was used after a careful evaluation of the curriculum requirements for the Data Science programs in the 136 colleges examined.…”

Section: Department/school Clustersmentioning

confidence: 99%

“…• This bottom up approach was used after a careful evaluation of the curriculum requirements for the Data Science programs in the 136 colleges examined. It was determined that several of the competencies presented in the 2021 ACM Task Force report [17] should be combined to align more directly with current Data Science degree programs.…”

Section: Department/school Clustersmentioning

confidence: 99%

“…• Table 5 below maps the seven competencies used in this research to the 2021 ACM Data Science Task Force report [17] competencies.…”

Section: Department/school Clustersmentioning

confidence: 99%

“…The program profile analysis includes; 1) College or university profile information: institution name, school name, type of institution ( private/public), 2) Major and department profile information: major name, department name, degree, 3) Credit profile information: accreditation, total number of credits for the degree, total number of credits for the core, 4) Instruction modality profile information: Online, In-person, Hybrid, and 5) Geographic profile information: city, state, geographic region (RQ1). The study also presents a comparative analysis of the competencies identified by the Association of Computing Machinery (ACM) in the 2021 Computing Competencies for Undergraduate Data Science Curricula Report [17] and those competencies identified in the 136 Data Science programs evaluated as part of the study (RQ2).…”

Section: Content Analysismentioning

confidence: 99%

See 2 more Smart Citations

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

Milonas

Li²,

Zhang

2021 ASEE Virtual Annual Conference Content Access Proceedings

View full text Add to dashboard Cite

What should we teach? A human‐centered data science graduate curriculum model design for iField schools

Sun

et al. 2022

Asso for Info Science & Tech

View full text Add to dashboard Cite

The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data-driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human-centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.

show abstract

Computing Competencies for Undergraduate Data Science Programs

Cited by 23 publications

References 1 publication

Report on the CyCAT winter school on fairness, accountability, transparency and ethics (FATE) in AI

Report on the CyCAT winter school on fairness, accountability, transparency and ethics (FATE) in AI

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

What should we teach? A human‐centered data science graduate curriculum model design for iField schools

Contact Info

Product

Resources

About