Overview

The NSF-funded Data Science Corps: Wrangle, Analyze, Visualize Experiential Learning Program is pleased to invite you and/or your colleagues to participate in the following upcoming events:

  • Data Science Symposium: Opportunities for Massachusetts Community Colleges
    Monday, June 13, 2022
  • Teaching Introductory Data Science in Massachusetts Community Colleges
    Tuesday, June 14 through Friday, June 17, 2022

Data Science Symposium

The National Academies of Science, Engineering, and Medicine (2018) consensus study, “Data Science for Undergraduates,” recommended that: “to prepare their graduates for this new data-driven era, academic institutions should encourage the development of a basic understanding of data science in all undergraduates.” We’ve been excited by how new courses and programs in data science have been flourishing nationwide. But much work remains to make this a reality.

Community colleges play a critical role in data science pathways for undergraduates. An introductory data science course is a necessary if not sufficient prerequisite to make this happen. As part of our work on the DSC-WAV we have had the opportunity to partner with three institutions (Springfield Technology Community College, Holyoke Community College, and Greenfield Community College) as they explore expansion in this growing area.

To broaden the discussions and activities that have been happening, we are planning a one-day Data Science Symposium as part of our week-long faculty development workshop entitled “Teaching Introductory Data Science in Massachusetts Community Colleges.”

We are inviting leadership (department chairs, deans, and presidents) to discuss the “State of the Commonwealth” in regards to associate’s to bachelor’s data science pathways in this one-day gathering.

Our goal for the symposium is to highlight barriers and identify creative solutions to facilitate teaching of a range of student-centered, pedagogically-sound introductory data science courses across the Commonwealth. We seek engaged faculty, chairs, and leadership to make connections across their campuses and across the state. Keynote speakers, project leadership, readings (B. S. Baumer and Horton 2023), and discussions will provide attendees with a rich understanding of the complex landscape of data science education in the Commonwealth, and illuminate possible forward paths.

The symposium will complement the program for the week-long workshop June 13–17th, 2022. We seek teams consisting of one or more senior leaders and between two to five faculty, with the latter group engaging for the rest of the week. However, individual faculty or senior leaders from an institution are welcomed to attend.

Please register your interest in attending as soon as possible and not later than April 25th. Depending on the response, participation may be limited.

More information about the DSC-WAV project can be found at https://dsc-wav.github.io/www or contact Andrea Dustin.

Workshop: Teaching Introductory Data Science in Massachusetts Community Colleges

Please see the workshop announcement for more information, and the FacDev ’22 website for updates.

Organizers

  • Benjamin Baumer, Smith College: PI on the DSC-WAV project, Ben has contributed to multiple curricular development efforts at the national level, including De Veaux et al. (2017), and B. Baumer (2015). He is a co-author of B. S. Baumer, Kaplan, and Horton (2021), one of the first comprehensive textbooks on data science.
  • Nicholas Horton, Amherst College: co-PI on the DSC-WAV project, Nick is a leader in the statistics and data science community (Horton, Baumer, and Wickham 2015; J. Hardin et al. 2015; J. S. Hardin and Horton 2017), and has worked to develop and promulgate curriculum guidelines for data science at two-year colleges. He is a co-author of B. S. Baumer, Kaplan, and Horton (2021).
  • Ethan Meyers, Hampshire College/Yale University: co-PI on the DSC-WAV project, Ethan has extensive experience in data science practice and education through his work at Hampshire College, Yale University, and through his research affiliation with the Center for Brains, Minds and Machines at MIT.

References

Baumer, Ben. 2015. “A Data Science Course for Undergraduates: Thinking with Data.” The American Statistician 69 (4): 334–42. http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2015.1081105.
Baumer, Benjamin S., and Nicholas J. Horton. 2023. “Data Science Transfer Pathways from Associate’s to Bachelor’s Programs.” Harvard Data Science Review 5 (1). https://doi.org/10.1162/99608f92.e2720e81.
Baumer, Benjamin S., Daniel T. Kaplan, and Nicholas J. Horton. 2021. Modern Data Science with R (2e). Chapman; Hall/CRC Press: Boca Raton. https://mdsr-book.github.io/mdsr2e.
De Veaux, Richard D., Mahesh Agarwal, Maia Averett, Benjamin S. Baumer, Andrew Bray, Thomas C. Bressoud, Lance Bryant, et al. 2017. “Curriculum Guidelines for Undergraduate Programs in Data Science.” Annual Review of Statistics and Its Application 4 (1): 1–16. https://doi.org/10.1146/annurev-statistics-060116-053930.
Hardin, Johanna S., and Nicholas J. Horton. 2017. “Ensuring That Mathematics Is Relevant in a World of Data Science.” Notices of the American Mathematical Society 64 (9): 986–90. https://www.ams.org/publications/journals/notices/201709/rnoti-p986.pdf.
Hardin, Johanna, Roger Hoerl, Nicholas J Horton, Deborah Nolan, Benjamin Baumer, Ofer Hall-Holt, Paul Murrell, et al. 2015. “Data Science in Statistics Curricula: Preparing Students to ‘Think with Data’.” The American Statistician 69 (4): 343–53. http://www.tandfonline.com/doi/abs/10.1080/00031305.2015.1077729.
Horton, Nicholas J., Benjamin S. Baumer, and Hadley Wickham. 2015. “Setting the Stage for Data Science: Integration of Data Management Skills in Introductory and Second Courses in Statistics.” CHANCE 28 (3): 40–50. http://chance.amstat.org/2015/04/setting-the-stage/.
National Academies of Science, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. National Academies Press: Washington, DC. https://nas.edu/envisioningds.
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