Integrating Computing in the Statistics and Data Science Curriculum: Creative Structures, Novel Skills and Habits, and Ways to Teach Computational Thinking

References

• Adams, B., Baller, D., Jonas, B., Joseph, A.-C., and Cummiskey, K. (2021), “Computational Skills for Multivariable Thinking in Introductory Statistics,” Journal of Statistics and Data Science Education, 29, 1–21, DOI: 10.1080/10691898.2020.1852139.
   Google Scholar
• American Statistical Association (2014), “Curriculum Guidelines for Undergraduate Programs in Statistical Science,” available at https://www.amstat.org/asa/education/Curriculum-Guidelines-for-Undergraduate-Programs-in-Statistical-Science.aspx.
   Google Scholar
• American Statistical Association (2016), “Guidelines for Assessment and Instruction in Statistics Education (GAISE) Revised College Report,” available at https://www.amstat.org/asa/education/Guidelines-for-Assessment-and-Instruction-in-Statistics-Education-Reports.aspx.
   Google Scholar
• Association for Computing Machinery Data Science Task Force (2019), “Computing Competencies for Undergraduate Data Science Curricula: Draft 2,” available at http://dstf.acm.org/DSReportDraft2Full.pdf. Accessed 2021 January 24.
   Google Scholar
• Beckman, M. D., Çetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., and Tackett, M. (2021), “Implementing Version Control With Git and GitHub as a Learning Objective in Statistics and Data Science Courses,” Journal of Statistics and Data Science Education, 29, 1–35, DOI: 10.1080/10691898.2020.1848485.
   Google Scholar
• Boehm, F. J., and Hanlon, B. M. (2021), “What Is Happening on Twitter? A Framework for Student Research Projects With Tweets,” Journal of Statistics and Data Science Education, 29, DOI: 10.1080/10691898.2020.1848486.
   Google Scholar
• Burckhardt, P., Nugent, R., and Genovese, C. R. (2021), “Teaching Statistical Concepts and Modern Data Analysis With a Computing-Integrated Learning Environment,” Journal of Statistics and Data Science Education, 29, 1–28, DOI: 10.1080/10691898.2020.1854637.
   Google Scholar
• Çetinkaya-Rundel, M., and Ellison, V. (2021), “A Fresh Look at Introductory Data Science,” Journal of Statistics and Data Science Education, 29, 1–11, DOI: 10.1080/10691898.2020.1804497.
   Google Scholar
• Cobb, G. (2015), “Mere Renovation Is Too Little Too Late: We Need to Rethink Our Undergraduate Curriculum From the Ground Up,” The American Statistician, 69, 266–282. DOI: 10.1080/00031305.2015.1093029.
   Web of Science ®Google Scholar
• Dogucu, M., and Çetinkaya-Rundel, M. (2021), “Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities,” Journal of Statistics and Data Science Education, 29, 1–11, DOI: 10.1080/10691898.2020.1787116.
   Google Scholar
• Donoghue, T., Voytek, B., and Ellis, S. E. (2021), “Teaching Creative and Practical Data Science at Scale,” Journal of Statistics and Data Science Education, 29, 1–22, DOI: 10.1080/10691898.2020.1860725.
   Google Scholar
• Gundlach, E., and Ward, M. D. (2021), “The Data Mine: Enabling Data Science Across the Curriculum,” Journal of Statistics and Data Science Education, 29, 1–14, DOI: 10.1080/10691898.2020.1848484.
   Google Scholar
• Kim, A. Y., and Hardin, J. (2021), “‘Playing the Whole Game’: A Data Collection and Analysis Exercise With Google Calendar,” Journal of Statistics and Data Science Education, 29, 1–10, DOI: 10.1080/10691898.2020.1799728.
   Google Scholar
• Kim, B., and Henke, G. (2021), “Easy-to-Use Cloud Computing for Teaching Data Science,” Journal of Statistics and Data Science Education, 29, 1–18, DOI: 10.1080/10691898.2020.1860726.
   Google Scholar
• National Academies of Science, Engineering, and Medicine (2018), “Data Science for Undergraduates: Opportunities and Options,” available at https://nas.edu/envisioningds.
   Google Scholar
• Nolan, D. A., and Temple Lang, D. (2010), “Computing in the Statistics Curriculum,” The American Statistician, 64, 97–107. DOI: 10.1198/tast.2010.09132.
   Web of Science ®Google Scholar
• Nolan, D. A., and Temple Lang, D. (2021), “Computing in the Statistics Curricula: A 10-Year Retrospective,” Journal of Statistics and Data Science Education, 29, DOI: 10.1080/10691898.2020.1862609.
   Google Scholar
• Reinhart, A., and Genovese, C. R. (2021), “Expanding the Scope of Statistical Computing: Training Statisticians to Be Software Engineers,” Journal of Statistics and Data Science Education, 29, 1–23, DOI: 10.1080/10691898.2020.1845109.
   Google Scholar
• Schwab-McCoy, A., Baker, C. M., and Gasper, R. E. (2021), “Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years,” Journal of Statistics and Data Science Education, 29, 1–17, DOI: 10.1080/10691898.2020.1851159.
   Google Scholar
• Theobold, A. S., Hancock, S. A., and Mannheimer, S. (2021), “Designing Data Science Workshops for Data-Intensive Environmental Science Research,” Journal of Statistics and Data Science Education, 29, 1–31, DOI: 10.1080/10691898.2020.1854636.
   Google Scholar
• Various (2015), “Discission Papers: Mere Renovation Is Too Little Too Late: We Need to Rethink Our Undergraduate Curriculum From the Ground Up,” The American Statistician, 69, available at https://nhorton.people.amherst.edu/mererenovation.
   Google Scholar
• Wing, J. M. (2006), “Computational Thinking,” Communications of the ACM, 49, 33–35. DOI: 10.1145/1118178.1118215.
   Web of Science ®Google Scholar
• Woodard, V., and Lee, H. (2021), “How Students Use Statistical Computing in Problem Solving,” Journal of Statistics and Data Science Education, 29, 1–18, DOI: 10.1080/10691898.2020.1847007.
   Google Scholar