Designing an Interdisciplinary Data Science Minor

Washington & Jefferson College, John Ladd & Rebecca Rapp

https://jrladd.com/slides/sigcse2025.html

Our starting point

Computing & Information Studies Major w/ Data Science Emphasis

  • CIS Minor
  • Computational Science Concentration

Why a data science minor?

  • DS Emphasis only available to existing CIS majors
  • many non-CIS majors were already voluntarily taking data science courses, encouraged by major advisors, most frequently:
    • business & economics
    • biology
    • environmental science
  • W&J’s two course of study requirement

A data science minor is a natural fit for a liberal arts college

  • Pairs well with most/all majors
  • Already an interdisciplinary, applied field
  • Can focus on teaching data science as a liberal art

Structuring the Program

Three required core courses

  • CIS 112 Database Concepts
  • CIS 241 Intro to Data Science
  • Statistics course (MTH 125, MTH 205, or MTH/BIO 245)

CIS 112 Database Concepts

  • covers data collection, organization
  • discusses user needs and user-centered DB design
  • data management concepts w/ SQL
  • also a core course for the CIS major

CIS 241 Intro to Data Science

  • covers data analysis and statistical modeling
  • focuses on applying data science to wide variety of fields/areas of study
  • not required for the CIS major but a popular introductory course
  • taught in Python with Pandas/sklearn

Statistics requirement

Students take one of the following:

  • MTH 125 Introductory Statistics
  • MTH 205 Probability & Statistics (Recommended)
  • MTH 245/BIO 245 Applied Stats for Life Sciences

Three more elective courses

  • Two must be CIS courses
  • One must be a Data Project course (see below)
  • One can (and should!) be outside of CIS

CIS Electives

  • CIS 220 Object-Oriented Programming
  • CIS 230 Artificial Intelligence
  • CIS 245 Information Visualization
  • CIS 343 Adv. Data Analysis: Simulations
  • CIS 345 Adv. Data Analysis: Networks

Selected non-CIS Electives (16 and counting)

  • BUS 326 Business Analytics
  • CHM 270 Analytical Chemistry
  • ECN 440 Econometrics
  • MTH 330 Introduction to Graph Theory
  • PHL 342 Experimental Philosophy
  • PHY 332 Electronics
  • POL 340 Research Methods
  • PSY 4XX Advanced Lab

Project Courses

  • Requires sustained, independent work by students on a data set of their choosing
  • Decided in conversation with faculty from different departments
  • Meant to ensure students have experience completing a data science project from start to finish
  • Students only need to complete 1, and both advanced CIS data courses count

Challenges

  • Continuing to coordinate and accommodate faculty/courses from a large number of disciplines
  • Helping students who take DS courses at different times in their college careers
  • Making students see that DS enhances the study of any discipline
  • Getting the word out to students not in a major that already has an elective
  • Showing the ways quantitative and qualitative analysis complement each other