Talks from the Joint Statistical Meetings 2018 (Vancouver, British Columbia)
Introductory Overview Lecture: Examining What and How We Teach at All Levels: Key Ideas to Ensure the Progress and Relevance of Statistics — Invited Special Presentation (JSM Partner Societies)
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-West 301
https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215836
Organizer(s): Rebecca Nugent, Carnegie Mellon University
Chair(s): Mine Cetinkaya-Rundel, Duke University
It has never been a better time to be a statistician. Demand for our profession continues to grow while the emergence of data science has invigorated both industry and academia. However, our education programs are simultaneously facing record numbers of students, the need to keep pace with the rapidly changing set of data-related tools and software development, and unparalleled diversity of career options. Innovation in education and training is a must at all levels, but it can seem daunting - where to begin? This IOL session will highlight changes in the national landscape for introductory level material and both undergraduate and graduate programs in statistics, biostatistics, and data science. We will give an overview of where we are as a field, emphasizing new ideas that could be adopted relatively smoothly, and provoke discussion about where we should be and what it will take to get there.
4:05 PM Introductory Statistics in a World of Data Science: Where We Are and Where We Need to Head — Nicholas J. Horton, Amherst College (slides)
4:35 PM Evolution of the Undergraduate Statistics Program — Rebecca Nugent, Carnegie Mellon University (slides)
5:05 PM Future of PhD Statistics/Biostatistics Education — Daniela Witten, University of Washington (slides)
5:35 PM Floor Discussion
Data Science Education - Successes and Challenges: Stories from the Classroom and Beyond — Invited Papers
Sun, 7/29/2018, 2:00 PM - 3:50 PM CC-West 202
Journal of Statistics Education , Business Analytics/Statistics Education Interest Group , Section on Statistical Education
Organizer(s): Soma Roy, Cal Poly, San Luis Obispo
Chair(s): Amy Wagler, The University of Texas at El Paso
2:05 PM Teaching Students to Think About Data Representation — Dennis L Sun, Cal Poly and Google (slides)
2:25 PM An Interdisciplinary Approach to Data Science Education — Galin Jones, University of Minnesota (slides)
2:45 PM Scaling a Data Science Curriculum to the Masses: Success and Failures in the Undergraduate Classroom — Thomas Fisher, Miami University (slides)
3:05 PM Data Science: a Recent Graduate's 'Reverse Engineered' Perspective — Kelsey Warsinske, DePauw University, Miami University, Facebook (slides)
3:25 PM Discussant: Nicholas J. Horton, Amherst College (slides)
Bringing Intro Stats into a Multivariate and Data-Rich World — Invited Papers (Section on Statistical Education)
https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215024
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West 206/207
Organizer(s): Jeff Witmer, Oberlin College
Chair(s): Ann Cannon, Cornell College
2:05 PM Inference in Three Hours, and More Time for the Good Stuff — Allen Downey, Olin College of Engineering (slides)
2:25 PM Multivariable Thinking with Data Visualization — Kari Lock Morgan, Pennsylvania State University (slides)
2:45 PM Multivariate Thinking and the Introductory Statistics Course: Preparing Students to Make Sense of a World Full of Observational Data — Nicholas J. Horton, Amherst College; Sarah C Anoke, Harvard TH Chan School of Public Health; Brendan Seto, Amherst College (slides)
3:05 PM Intro Stats and Intro Data Science: Do We Need Both? — Mine Cetinkaya-Rundel, Duke University (slides)
3:25 PM Discussant: Jeff Witmer, Oberlin College
3:45 PM Floor Discussion
- Cetinkaya-Rundel and Rundel (2018) "Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum", https://amstat.tandfonline.com/doi/full/10.1080/00031305.2017.1397549
- Electronic Conference on Teaching Statistics (2018), https://www.causeweb.org/cause/ecots/ecots18/program
- GAISE College report (2016) "Guidelines for Assessment and Instruction in Statistics Education", http://www.amstat.org/asa/education/Guidelines-for-Assessment-and-Instruction-in-Statistics-Education-Reports.aspx
- Horton and Hardin (2015) "Teaching the Next Generation of Statistics Students to 'Think With Data': Special Issue on Statistics and the Undergraduate Curriculum", The American Statistician, https://amstat.tandfonline.com/doi/full/10.1080/00031305.2015.1094283
- Mosaic Resources (2018) "Resources Related to the mosaic Package", https://cran.r-project.org/web/packages/mosaic/vignettes/mosaic-resources.html
- National Academy of Sciences (2018) "Undergraduate Data Science: Opportunities and Options" https://nas.edu/envisioningds
- Nolan and Temple Lang (2010) "Computing in the Statistics Curriculum", *The American Statistician", https://amstat.tandfonline.com/doi/abs/10.1198/tast.2010.09132
- Pruim, Kaplan, and Horton (2017) "The mosaic Package: Helping Students to Think with Data Using R", RJournal, https://journal.r-project.org/archive/2017/RJ-2017-024/index.html
- Robinson (2017) "Teach the tidyverse to beginners", http://varianceexplained.org/r/teach-tidyverse/ (see also https://cran.r-project.org/web/packages/ggformula/vignettes/ggformula-blog.html)
- Wang (2017) "Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often", Technology Innovations in Statistics Education, https://escholarship.org/uc/item/84v3774z
See also https://github.com/beanumber/jsm2018 for materials from "Expanding the Tent: Undergraduate Majors in Data Science"
Last updated August 5, 2018