Draft:Data science education

Submission declined on 15 March 2024 by Guessitsavis (talk).

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Declined by Guessitsavis 2 months ago. Last edited by Guessitsavis 2 months ago. Reviewer: Inform author.

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Comment: Might be a candidate for inclusion into Data science. Sincerely, Guessitsavis (she/they) (Talk) 12:26, 15 March 2024 (UTC)

Data science is a practical and research domain focuses on extracting value and knowledge from data. Data science education is, accordingly, a domain that focuses on data science curricula and pedagogy or, in other words, on answering the questions “What should be taught teach in data science programs?” and “What is a suitable pedagogy for teaching data science?”.^[1]. Data science education is gaining growing importance as the need for data scientists increases and the need for data literacy expands to more and more populations^[2], especially today when generative-AI is becoming more and more pervasive.

Data science is a new discipline in the collection of STEM disciplines. While gender imbalances are a well-known challenge in STEM disciplines, and specifically in computer science, data science may promote gender balance in STEM disciplines due to its interdisciplinary nature^[3].

Main research topics in data science education[edit]

A survey of 1048 papers regarding data science education revealed that the main research topics of data science education can be categorized into (a) data science curricula, (b) data science pedagogy, (c) STEM skills, (d) domain adaptation, and (e) social aspects of data science aspects^[4]

The data science curriculum category refers to the question “What should be taught in data science programs”. Research focuses on principles of data science curriculum design, approaches to data science education, the Introduction to Data Science course, data science programs for data science majors^[5], and data science for K-12 pupils.

The data science pedagogy category refers to the efficient methods of teaching data science. The research focuses on teaching AI and machine learning, general teaching methods for data science, online teaching, and tools and methods for data science education.

The STEM skills category refers to the integration and interaction between statistics, computer science, and data science^[6]. Both the statistical and computer science skills that are required within the context of data science, as well as the teaching of data science within the context of statistics and computer science, are discussed.

The domain adaptation category refers to the challenges of teaching data science within the context of specific domain knowledge, such as in business, health, digital technologies, biomedicine, education and more.

The social aspects of data science category is attracting growing attention and includes the topics and methods of teaching ethics, teaching data science as a skill, enhancing student engagement, and enhancing diversity in data science

References[edit]

^ Mike, Koby (2020-08-07). "Data Science Education: Curriculum and pedagogy". Proceedings of the 2020 ACM Conference on International Computing Education Research. New York, NY, USA: ACM. pp. 324–325. doi:10.1145/3372782.3407110. ISBN 978-1-4503-7092-9. S2CID 221035146.
^ Dichev, Christo; Dicheva, Darina (2017). "Towards Data Science Literacy". Procedia Computer Science. 108: 2151–2160. doi:10.1016/j.procs.2017.05.240. ISSN 1877-0509.
^ Berman, Francine D.; Bourne, Philip E. (2015-07-27). "Let's Make Gender Diversity in Data Science a Priority Right from the Start". PLOS Biology. 13 (7): e1002206. doi:10.1371/journal.pbio.1002206. ISSN 1545-7885.
^ Mike, Koby; Kimelfeld, Benny; Hazzan, Orit (2023-10-27). "The Birth of a New Discipline: Data Science Education". Harvard Data Science Review. 5 (4). doi:10.1162/99608f92.280afe66. ISSN 2644-2353.
^ "Envisioning the Data Science Discipline". National Academies of Sciences, Engineering, and Medicine. 2018-03-05. doi:10.17226/24886. ISBN 978-0-309-46502-1.
^ ACM Data Science Task Force (2021-01-15). Computing competencies for undergraduate data science curricula. New York, NY, USA: ACM. doi:10.1145/3453538. ISBN 978-1-4503-9060-6.

[1] Mike, Koby (2020-08-07). "Data Science Education: Curriculum and pedagogy". Proceedings of the 2020 ACM Conference on International Computing Education Research. New York, NY, USA: ACM. pp. 324–325. doi:10.1145/3372782.3407110. ISBN 978-1-4503-7092-9. S2CID 221035146.

[2] Dichev, Christo; Dicheva, Darina (2017). "Towards Data Science Literacy". Procedia Computer Science. 108: 2151–2160. doi:10.1016/j.procs.2017.05.240. ISSN 1877-0509.

[3] Berman, Francine D.; Bourne, Philip E. (2015-07-27). "Let's Make Gender Diversity in Data Science a Priority Right from the Start". PLOS Biology. 13 (7): e1002206. doi:10.1371/journal.pbio.1002206. ISSN 1545-7885.

[4] Mike, Koby; Kimelfeld, Benny; Hazzan, Orit (2023-10-27). "The Birth of a New Discipline: Data Science Education". Harvard Data Science Review. 5 (4). doi:10.1162/99608f92.280afe66. ISSN 2644-2353.

[5] "Envisioning the Data Science Discipline". National Academies of Sciences, Engineering, and Medicine. 2018-03-05. doi:10.17226/24886. ISBN 978-0-309-46502-1.

[6] ACM Data Science Task Force (2021-01-15). Computing competencies for undergraduate data science curricula. New York, NY, USA: ACM. doi:10.1145/3453538. ISBN 978-1-4503-9060-6.

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