1932

Abstract

Organizing a graduate program in statistics and data science raises many questions, offering a variety of opportunities while presenting a multitude of choices. The call for graduate programs in statistics and data science is overwhelming. How does it align with other (future) study programs at the secondary and postsecondary levels? What could or should be the natural home for data science in academia? Who meets the entry criteria, and who does not? Which strategic choices inevitably play a prominent role when developing a curriculum? We share our views on the why, when, where, who and what.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-040620-032820
2021-03-07
2024-04-29
Loading full text...

Full text loading...

/deliver/fulltext/statistics/8/1/annurev-statistics-040620-032820.html?itemId=/content/journals/10.1146/annurev-statistics-040620-032820&mimeType=html&fmt=ahah

Literature Cited

  1. Cleveland WS. 2014. Data science: an action plan for expanding the technical areas of the field of statistics. Stat. Anal. Data Min. 7:414–17
    [Google Scholar]
  2. Davenport TH, Patil DJ. 2012. Data scientist: the sexiest job of the 21st century. Harv. Bus. Rev. 90:1070–76
    [Google Scholar]
  3. De Veaux RD, Agarwal M, Averett M, Baumer B, Bray A et al. 2017. Curriculum guidelines for undergraduate programs in data science. Annu. Rev. Stat. Appl. 4:15–30
    [Google Scholar]
  4. Donoho D. 2017. 50 years of data science. J. Comput. Graph. Stat. 26:4745–66
    [Google Scholar]
  5. Dwork C, Roth A. 2014. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9:211–407
    [Google Scholar]
  6. Elliott AC, Stokes L, Cao J 2018. Teaching ethics in a statistics curriculum with a cross-cultural emphasis. Am. Stat. 72:4359–67
    [Google Scholar]
  7. Hallinen J. 2019. STEM education curriculum. Encyclopedia Britannica https://www.britannica.com/topic/STEM-education
    [Google Scholar]
  8. Hardin J, Hoerl R, Horton N, Nolan D, Baumer B et al. 2015. Data science in statistics curricula: preparing students to “think with data.”. Am. Stat. 69:4343–53
    [Google Scholar]
  9. Heinemann B, Opel S, Budde L, Schulte C, Frischemeier D et al. 2018. Drafting a data science curriculum for secondary schools. Koli Calling '18: Proceedings of the 18th Koli Calling International Conference on Computing Education Research https://doi.org/10.1145/3279720.3279737
    [Crossref] [Google Scholar]
  10. Hernán MA, Hsu J, Healy B 2019. A second chance to get causal inference right: a classification of data science tasks. Chance 32:142–49
    [Google Scholar]
  11. Hicks SC, Irizarry RA. 2018. A guide to teaching data science. Am. Stat. 72:4382–91
    [Google Scholar]
  12. Horton NJ, Hardin JS 2015. Am. Stat. 69:4)
  13. Huppenkothen D, Arendt A, Hogg DW, Ram K, VanderPlas J, Rokem A 2018. Hack weeks as a model for data science education and collaboration. PNAS 115:368872–77
    [Google Scholar]
  14. Kane MJ. 2014. Commentary: Cleveland's action plan and the development of data science over the last 12 years. Stat. Anal. Data Min. 7:6423–24
    [Google Scholar]
  15. Keller SA, Shipp S, Schroeder A 2016. Does big data change the privacy landscape? A review of the issues. Annu. Rev. Stat. Appl. 3:161–80
    [Google Scholar]
  16. Koomen MH, Rodriguez E, Hoffman A, Petersen C, Oberhauser K 2018. Authentic science with citizen science and student-driven science fair projects. Sci. Educ. 102:3593–644
    [Google Scholar]
  17. Mikroyannidis A, Domingue J, Phethean C, Beeston G, Simperl E 2018. Designing and delivering a curriculum for data science education across Europe. ICL 2017: Teaching and Learning in a Digital World ME Auer, D Guralnick, I Simonics 540–50 New York: Springer
    [Google Scholar]
  18. Pittard V. 2018. The integration of data science in the primary and secondary curriculum Rep., R. Soc London: https://royalsociety.org/topics-policy/publications/2018/integration-data-science-in-primary-secondary-curriculum/
  19. Rubin DB. 1987. Multiple Imputation for Nonresponse in Surveys New York: Wiley
  20. Saltz JS, Dewar NI, Heckman R 2018. Key concepts for a data science ethics curriculum. SIGCSE'18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education952–57 New York: ACM
    [Google Scholar]
  21. Wild CJ, Pfannkuch M. 1999. Statistical thinking in empirical enquiry. Int. Stat. Rev. 67:223–65
    [Google Scholar]
  22. Yavuz FG, Ward MD. 2020. Fostering undergraduate data science. Am. Stat. 74:8–16
    [Google Scholar]
  23. Zheng T. 2017. Teaching data science in a statistical curriculum: Can we teach more by teaching less. ? J. Comput. Graph. Stat. 26:4772–74
    [Google Scholar]
/content/journals/10.1146/annurev-statistics-040620-032820
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error