A Fresh Look at Introductory Data Science

References

• Baumer, B. (2015), “A Data Science Course for Undergraduates: Thinking With Data,” The American Statistician, 69, 334–342, DOI: 10.1080/00031305.2015.1081105.
   Web of Science ®Google Scholar
• Baumer, B., Çetinkaya-Rundel, M., Bray, A., Loi, L., and Horton, N. J. (2014), “R Markdown: Integrating a Reproducible Analysis Tool Into Introductory Statistics,” Technology Innovations in Statistics Education [online], 8. Available at https://escholarship.org/uc/item/90b2f5xh.
   Google Scholar
• Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P., and Deckmyn, A. (2018), “maps: Draw Geographical Maps,” R Package Version 3.3.0, available at https://CRAN.R-project.org/package=maps.
   Google Scholar
• Beckman, M. D., Çetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., and Tackett, M. (2020), “Implementing Version Control With Git as a Learning Objective in Statistics Courses,” arXiv no. 2001.01988.
   Google Scholar
• Brunner, R. J., and Kim, E. J. (2016), “Teaching Data Science,” Procedia Computer Science, 80, 1947–1956.
   Google Scholar
• Bryan, J. (2018), “Excuse Me, Do You Have a Moment to Talk About Version Control?,” The American Statistician, 72, 20–27.
   Web of Science ®Google Scholar
• Carver, R., Everson, M., Gabrosek, J., Horton, N., Lock, R., Mocko, M., Rossman, A., Roswell, G. H., Velleman, P., Witmer, J., and Wood, B. (2016), “Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016,” available at https://www.amstat.org/asa/files/pdfs/GAISE/GaiseCollege/_Full.pdf.
   Google Scholar
• Çetinkaya-Rundel, M. (2018), “Let Them Eat Cake First!,” available at https://www.youtube.com/watch?v=RsVOrpXAPXo.
   Google Scholar
• Çetinkaya-Rundel, M., and Rundel, C. (2018), “Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum,” The American Statistician, 72, 58–65, DOI: 10.1080/00031305.2017.1397549.
   Web of Science ®Google Scholar
• Çetinkaya-Rundel, M., and Tackett, M. (2020), “From Drab to Fab: Teaching Visualization via Incremental Improvements,” CHANCE, 33, 31–41.
   Google Scholar
• Chang, W., Cheng, J., Allaire, J., Xie, Y., and McPherson, J. (2020), “shiny: Web Application Framework for R,” R Package Version 1.5.0, available at https://CRAN.R-project.org/package=shiny.
   Google Scholar
• Danyluk, A., Leidig, P., Cassel, L., and Servin, C. (2019), “ACM Task Force on Data Science Education: Draft Report and Opportunity for Feedback,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education, available at http://dstf.acm.org/DSReportInitialFull.pdf.
   Google Scholar
• De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., Bryant, L., Cheng, L. Z., Francis, A., Gould, R., and Kim, A. Y. (2017), “Curriculum Guidelines for Undergraduate Programs in Data Science,” Annual Review of Statistics and Its Application, 4, 15–30.
   Web of Science ®Google Scholar
• Dichev, C., and Dicheva, D. (2017), “Towards Data Science Literacy,” in ICCS, pp. 2151–2160.
   Google Scholar
• Dichev, C., Dicheva, D., Cassel, L., Goelman, D., and Posner, M. (2016), “Preparing All Students for the Data-Driven World,” in Proceedings of the Symposium on Computing at Minority Institutions, ADMI (Vol. 346).
   Google Scholar
• England, B. J., Brigati, J. R., and Schussler, E. E. (2017), “Student Anxiety in Introductory Biology Classrooms: Perceptions About Active Learning and Persistence in the Major,” PLoS One, 12, e0182506. DOI: 10.1371/journal.pone.0182506.
   PubMed Web of Science ®Google Scholar
• Fiksel, J., Jager, L. R., Hardin, J. S., and Taub, M. A. (2019), “Using GitHub Classroom to Teach Statistics,” Journal of Statistics Education, 27, 110–119.
   Web of Science ®Google Scholar
• Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Temple Lang, D., and Ward, M. D. (2015), “Data Science in Statistics Curricula: Preparing Students to ‘Think With Data’,” The American Statistician, 69, 343–353, DOI: 10.1080/00031305.2015.1077729.
   Web of Science ®Google Scholar
• Henry, L., and Wickham, H. (2020), “purrr: Functional Programming Tools,” R Package Version 0.3.4, available at https://CRAN.R-project.org/package=purrr.
   Google Scholar
• Hicks, S. C., and Irizarry, R. A. (2018), “A Guide to Teaching Data Science,” The American Statistician, 72, 382–391. DOI: 10.1080/00031305.2017.1356747.
   PubMed Web of Science ®Google Scholar
• Iannone, R., Allaire, J., and Borges, B. (2020), “flexdashboard: R Markdown Format for Flexible Dashboards,” R Package Version 0.5.2, available at https://CRAN.R-project.org/package=flexdashboard.
   Google Scholar
• Kaggle (2017), “Kaggle Machine Learning & Data Science Survey 2017,” available at https://www.kaggle.com/kaggle/kaggle-survey-2017.
   Google Scholar
• Knuth, D. E. (1984), “Literate Programming,” The Computer Journal, 27, 97–111.
   Web of Science ®Google Scholar
• Kuhn, M., and Wickham, H. (2020), “tidymodels: Easily Install and Load the ‘Tidymodels’ Packages,” R Package Version 0.1.0, available at https://CRAN.R-project.org/package=tidymodels.
   Google Scholar
• Michaelsen, L. K., and Sweet, M. (2011), “Team-Based Learning,” New Directions for Teaching and Learning, 2011, 41–51.
   Google Scholar
• NASEM (2018), Data Science for Undergraduates, Opportunities and Options, Washington, DC: The National Academies Press.
   Google Scholar
• Nolan, D., and Temple Lang, D. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107, DOI: 10.1198/tast.2010.09132.
   Web of Science ®Google Scholar
• NSF (2014), “Data Science at NSF Draft Report of StatSNSF Committee: Revisions Since January MPSAC Meeting,” available at https://www.nsf.gov/attachments/130849/public/Stodden-StatsNSF.pdf.
   Google Scholar
• Parry, J., and Barr, N. (2020), “genius: Easily Access Song Lyrics From Genius.com,” R Package Version 2.2.2, available at https://CRAN.R-project.org/package=genius.
   Google Scholar
• QS World University Rankings (2017), “QS World University Rankings for Statistics and Operational Research 2017,” available at https://www.topuniversities.com/university-rankings/university-subject-rankings/2017/statistics-operational-research.
   Google Scholar
• R Core Team (2020), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, available at https://www.R-project.org.
   Google Scholar
• Robinson, D. (2017), “unvotes: United Nations General Assembly Voting Data,” R Package Version 0.2.0, available at https://CRAN.R-project.org/package=unvotes.
   Google Scholar
• Robinson, D. (2019), “gutenbergr: Download and Process Public Domain Works From Project Gutenberg,” R Package Version 0.1.5, available at https://CRAN.R-project.org/package=gutenbergr.
   Google Scholar
• Robinson, D., and Hayes, A. (2020), “broom: Convert Statistical Analysis Objects Into Tidy Tibbles,” R Package Version 0.5.6, available at https://CRAN.R-project.org/package=broom.
   Google Scholar
• Rundel, C., Çetinkaya-Rundel, M., and Anders, T. (2019), “ghclass: Tools for Managing Classes With GitHub,” R Package Version 0.1.0, available at https://rundel.github.io/ghclass.
   Google Scholar
• Sahami, M., Danyluk, A., Fincher, S., Fisher, K., Grossman, D., Hawthorne, E., Katz, R., LeBlanc, R., Reed, D., Roach, S., and Cuadros-Vargas, E. (2013), “Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science,” in Association for Computing Machinery (ACM)-IEEE Computer Society.
   Google Scholar
• Silge, J., and Robinson, D. (2016), “tidytext: Text Mining and Analysis Using Tidy Data Principles in R,” Journal of Open Source Software, 1, 37, DOI: 10.21105/joss.00037.
   Google Scholar
• U.S. News & World Report (2018), “Best Statistics Graduate Programs—Top Science Schools,” available at https://www.usnews.com/best-graduate-schools/top-science-schools/statistics-rankings.
   Google Scholar
• Wang, X., Rush, C., and Horton, N. J. (2017), “Data Visualization on Day One: Bringing Big Ideas Into Intro Stats Early and Often,” Technology Innovations in Statistics Education, 10. Available at https://escholarship.org/uc/item/84v3774z.
   Google Scholar
• Wickham, H. (2019), “rvest: Easily Harvest (Scrape) Web Pages,” R Package Version 0.3.5, available at https://CRAN.R-project.org/package=rvest.
   Google Scholar
• Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., and Kuhn, M. (2019), “Welcome to the Tidyverse,” Journal of Open Source Software, 4, 1686.
   Google Scholar
• Wickham, H., and Grolemund, G. (2016), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, Sebastopol, CA: O’Reilly Media, Inc., available at https://r4ds.had.co.nz.
   Google Scholar
• Wilson, S. G. (2013), “The Flipped Class: A Method to Address the Challenges of an Undergraduate Statistics Course,” Teaching of Psychology, 40, 193–199.
   Web of Science ®Google Scholar
• Winquist, J. R., and Carlson, K. A. (2014), “Flipped Statistics Class Results: Better Performance Than Lecture Over One Year Later,” Journal of Statistics Education, 22, DOI: 10.1080/10691898.2014.11889717.
   Google Scholar
• Xie, Y., Allaire, J. J., and Grolemund, G. (2018), R Markdown: The Definitive Guide, Boca Raton, FL: Chapman and Hall/CRC.
   Google Scholar