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Development of IPSJ Data Science Curriculum Standard

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Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 642))

Abstract

The Information Processing Society of Japan (IPSJ) published a curriculum standard for university-level education majoring in data science (DS) (An English translation of the IPSJ Data Science Curriculum Standard is available at: https://www.ipthree.org/wp-content/uploads/IPSJ-DS-Curriculum_202104_en.pdf). In this paper, we shall report strategy and development of the DS curriculum standards. Data science education and the development of data scientists are recognised to be quite important in both social and business contexts. The IPSJ DS curriculum standard is developed by integrating various related initiatives and has the following unique features. (1) The DS curriculum standard covers a wide range of related fields to ensure international compatibility through mapping to the ACM Data Science curriculum and the European EDISON Data Science Framework. (2) It collaborates with the IPSJ Data Scientist certification (under development) by referring to the Data Scientist skill checklist (assistant level) developed by the Data Scientist Society of Japan. (3) It clarifies the knowledge and skills required of students majoring in data science. (4) It assigns a time to each educational content so that the curriculum size becomes approximately 675 class hours (60 credits in the Japanese credit system). (5) It collaborates with the Model Curriculum for Mathematics, Data Science and AI Education (literacy level) developed by the Japan Inter-University Consortium for Mathematics and Data Science, supported by the Japanese government.

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References

  1. Integrated Innovation Strategy Promotion Council of Japan: AI Strategy (2019). https://www.kantei.go.jp/jp/singi/ai_senryaku/pdf/aistratagy2019en.pdf

  2. Information Processing Society of Japan: J17 Computing Curriculum Standard (in Japanese) (2017). https://www.ipsj.or.jp/annai/committee/education/j07/curriculum_j17.html

  3. Information Processing Society of Japan: Data Scientist Strategy (in Japanese) (2021). https://www.ipsj.or.jp/release/20210413_DSstrategy.html

  4. Japan Inter-University Consortium for Mathematics & Data Science. http://www.mi.u-tokyo.ac.jp/consortium/en/

  5. Japan Inter-University Consortium for Mathematics & Data Science: Model Curriculum for Mathematics, Data Science and AI Education (Literacy) - Learning Data-Thinking (in Japanese) (2020). http://www.mi.u-tokyo.ac.jp/consortium/model_literacy.html

  6. ACM Data Science Task Force: Computing Competencies for Undergraduate Data Science Curricula (2021). http://dstf.acm.org/

  7. EDISON Data Science Framework. https://edisoncommunity.github.io/EDSF/

  8. The Japan Data Scientist Society: Data Scientist Skill Checklist (in Japanese) (2019). https://www.datascientist.or.jp/common/docs/skillcheck.xlsx

  9. IPA: IT Skill Standard. https://www.ipa.go.jp/english/humandev/forth_download.html

  10. SFIA Foundation: Skills Framework for the Information Age, Version 8. https://sfia-online.org/en

  11. IPA: Registered Information Security Specialist Examination. https://www.ipa.go.jp/files/000009645.pdf

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Acknowledgement

This research is supported by JSPS Kakenhi #20K03232.

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Correspondence to Tetsuro Kakeshita .

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Kakeshita, T. et al. (2022). Development of IPSJ Data Science Curriculum Standard. In: Passey, D., Leahy, D., Williams, L., Holvikivi, J., Ruohonen, M. (eds) Digital Transformation of Education and Learning - Past, Present and Future. OCCE 2021. IFIP Advances in Information and Communication Technology, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-030-97986-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-97986-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97985-0

  • Online ISBN: 978-3-030-97986-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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