Two-Year College Data Science Summit

May 10-11, 2018, Washington, DC metro area
Agenda
Participants
View April 18 TYCDSS Introductory Webinar

With funding from the National Science Foundation, this workshop will bring together a diverse group of participants to make recommendations for two-year college data science programs, keeping in mind the needs of each of three student populations:

  1. Those seeking employment following an associate’s degree
  2. Those seeking transfer to four-year programs
  3. Those seeking certificate programs and college-level courses in data science for professional development

The following products are the desired outputs of the summit:

  1. Report summarizing current state of data science/analytics programs at two-year colleges, including the challenges in establishing such programs at two-year colleges
  2. Guidelines and learning outcomes for two-year college data science/analytics programs for the three potential student populations listed above
  3. Resources for two-year colleges that are considering creating a data science/analytics program.

Capacity for the workshop has been reached but please fill out this Google form to be updated on developments, discussions, and products.

The steering committee includes the following:

  • Rob Gould, chair (2016–2017), ASA/AMATYC Joint Committee; director, UCLA Center for Teaching Statistics; professor of statistics, UCLA; co-author of PCMI Guidelines; participant in ODI Global Data Literacy workshop
  • Beth Hawthorne, chair, ACM Committee for Computing Education in Community Colleges; vice chair, ACM Education Board; community college representative to the ACM Education Policy Committee; senior professor of computer science, Union County College
  • Nicholas Horton, professor of mathematics and statistics, Amherst College; NAS data science education roundtable member
  • Randy Kochevar, co-principal investigator, ODI Pathways for Big Data Careers; director, Oceans of Data Institute
  • Brian Kotz, professor of mathematics and statistics and Data Science Development Team lead, Montgomery College
  • Roxy Peck, vice chair, ASA Education Council; professor emeritus, Department of Statistics, California Polytechnic State University, San Luis Obispo
  • Mary Rudis, director of Practical Data Science Program and professor of mathematics, Great Bay Community College
  • Brad Thompson, Instructor, Mathematics and Statistics, Delaware Technical Community College; Instructional Designer, Center for Creative Instruction & Technology, DTCC.
  • Heikki Topi, principal investigator, Exploring the Status of Education for Data Science Workshop, October 2015; professor of computer information systems, Bentley University

ASA Director of Strategic Initiatives and Outreach Donna LaLonde, ASA Director of Education Rebecca Nichols, and ASA Director of Science Policy Steve Pierson will offer support from the American Statistical Association.

We will update this webpage with additional materials as they become available. For questions, email Steve Pierson. Also, if your two-year college data science program or course is not included in our list, please let Steve know.

We thank the sponsors of this workshop:

BAH
GW

To become a sponsor, contact Steve Pierson.

Resources
  1. Curriculum Guidelines for Undergraduate Programs in Data Science, Park City Math Institute 2016 Summer Undergraduate Faculty Program
  2. National Academy of Science Roundtable on Data Science Postsecondary Education
  3. National Academy of Science Envisioning the Data Science Discipline
  4. Oceans of Data Tools for Building a Big Data Career Path
  5. Oceans of Data Profile of the Data Practitioner
  6. Strengthening Data Science Education Through Collaboration, Report on a Workshop on Data Science Education Funded by the National Science Foundation, Award #: DOE 1545135
  7. Data Science is for Everyone, Plenary Talk, Sallie Keller, Social and Decision Analytics Laboratory, Virginia Tech
  8. AMATYC, ASA, & CAUSE links
  9. Data Science/Analytics Courses/Programs in Two-Year Colleges
  10. AMATYC Data Science Resources Page