Dec 04, 2024  
[DRAFT] Course Catalog 2025-2026 
    
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[DRAFT] Course Catalog 2025-2026 [ARCHIVED CATALOG]

Data Science Integrative Concentration

The integrative concentration consists of a minimum of 5 full courses (or the equivalent), 1 experiential component, and 1 learning portfolio.

Note: Students must earn minimum grades of C- or P for all courses that apply toward the integrative concentration.

Data science is the study of principled, scientific methods for collecting, managing, analyzing, and decision-making using data. It synergizes and builds upon the computational problem solving of computer science, the analytical skills of statistics and mathematics, and the designed data collection and experimentation of the natural and social sciences to unlock insights and knowledge from large- and small-scale data in diverse mediums (e.g., numbers, text, audio, images, and video).

arrow Visit the concentration’s web page for more information.

Summary of Requirements

Note(s) on Requirements

  • If a student wishes to count a course that is not listed below toward the concentration, they can petition the concentration chair(s) for approval to apply the completed or in-progress course toward their concentration.
  • In planning their schedules, students should be aware that some of the courses listed below have prerequisites.

Declaring the Integrative Concentration

Students wishing to complete the data science integrative concentration should consult with a member of the concentration advisory group and complete the integrative concentration declaration/change form. The form requires the signature of one of the concentration co-chairs.

Co-Chairs
Joshua H. Davidson, Assistant Professor of Statistics and Data Science
Adam D. Eck, David H. and Margaret W. Barker Associate Professor of Computer Science

arrow See the full list of Data Science Integrative Concentration Advisory Group members.

Detailed Integrative Concentration Requirements

Data Science Integrative Concentration Course Lists

Introduction to Data and Computing Courses

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Statistics Courses

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Data Science Methods Courses

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Applications of Data Science Courses

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With the approval of the concentration chair, a private reading course in a partner department could count for this requirement (if it is not being used to fulfill the experiential component requirement).

Experiential Component

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The experiential component of the data science integrative concentration can be fulfilled in a variety of ways:

  • Internship in industry, NGO, or non-profit (verified by concentration chair)
  • Undergraduate research (verified by concentration chair and research advisor that it involves data science as a central component of the research)
  • Honors project (verified by concentration chair and honors advisor that data science plays a central role)
  • Group Winter Term project in applied data science
  • Individual Winter Term projects (verified by concentration chair)
Duration of the Experience

The experiential component requires approximately 150 total hours of experience, which could include a combination of:

  • Two Winter Term projects (each three weeks at 25 hours/week)
  • One summer internship/research project (six weeks at 25 hours/week)
  • One semester of full course credit research (10 hours/week, four credit hours)
  • Two semesters of half course credit research (each five hours/week, two credit hours)

Learning Portfolio

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Students will be required to maintain a learning portfolio which will include signature course work as well as pre- and post-internship reflection. The portfolio is designed to support students’ appreciation of business as an area of rich intellectual engagement, as well as how to launch from college to career. Vital to this integration is the student’s understanding of how the range of skills acquired through liberal arts learning are transferable to the workplace. The integrative component will be overseen by the student’s faculty advisor for the concentration.

​​​​​​​The learning portfolio will consist of the following:

  • Major coursework (e.g., large assignments and final projects)
  • Reflective essay describing what was learned during the experiential learning component, how it changed the student’s view of data science, and the career pathways it illuminated.
  • A presentation communicating the results of applying data science in a project, given one of two formats:
    • A 15 minute video presentation of a data science project (e.g, final project for a course or honors presentation), OR
    • A Senior Symposium presentation

Data Science Integrative Concentration Advisory Group

Joshua H. Davidson, Assistant Professor of Statistics and Data Science; co-chair
Adam D. Eck, David H. and Margaret W. Barker Associate Professor of Computer Science; co-chair

Nancy E. Darling, Professor of Psychology
Aaron D. Goldman, Associate Professor of Biology
Jeffrey A. Witmer, Professor of Statistics