Serving Computing and Data Science Needs in a Liberal Arts Setting

Contributed by Karl Schmitt, karl.schmitt@valpo.edu

Institutional and departmental context

Institution Name: Valparaiso University

• Location: Valparaiso, Indiana
• Student Body Size: 3500-4500 Undergraduates, 500-1000 Graduate Students
• Degrees Offered:
  • B.A., B.S. Computer Science
  • Master of Science in Information Technology
  • Master of Science in Cybersecurity
  • B.S. in Data Science**
• Department/Major Name:
  • Department of Computing and Information Sciences (CIS)
    • Note: I’m an affiliate member of this department as a full participant
  • Department of Mathematics and Statistics (MST)**
    • This is my official department home
• Number of Contributing Faculty:
  • CIS:
    • 4 Full-time Tenure lines
    • 4 Full-time/continuous lecture lines
    • Additional adjuncts
  • MST:
    • 13 Full-time tenure lines + 1 full-time admin (assoc. provost)
    • 2 are full-time statisticians
    • 2 Full-time lecture positions
• Number of Majors Annually:
  • CIS Undergraduate: 10-20 (per class year)
  • Data Science: 3-5 (per class year)
  • Graduate: Highly variable

Facilitation

Are you willing to facilitate this discussion? Yes

Are there any others whom you would recommend as potential facilitators for this topic?

• Jessen Havill from Denison University
• Nicholas Rosasco generally attends SIGCSE (as do other members of CIS occasionally)

Description

With the continued growth of Data Science as a discipline there is a new, though similar, issue of serving a wide-range of students, both majors and non-majors. Specifically, a new challenge has been given to computer science departments to serve ‘almost’ majors. Beyond simply a “non-major” data science majors need a significant amount of computing courses yet may not (or may) plan to fully major in computer science. Moreover, the needs of a data science student, while more like a computer science major than other non-majors, still differ from a traditional computer scientist.

This discussion will spend time thinking about what is different, and the same, in terms of teaching data science majors along-side computer science majors. If your school can offer multiple sections, where should they go (the major’s course? The non-major’s course?). If your school offers a data-science degree, but NOT in the computer science department, how strongly should you be advocating for involvement? Should Computer Science departments be offering “Introductory Data Science” courses? As an upper-level elective? For all students? What are some of the concerns in terms of relating with Mathematics or Statistics Departments?