Data Science Integrative Concentration

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.

Data Science Integrative Concentration Course Lists

Experiential Component

Return to the summary of requirements.

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)
  • Students are encouraged to seek an internship through a relevant career community in such areas as Business, Finance, and Consulting; Medical, Public, and Global Health Professions; and Science and Technology.
• 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

Return to the summary of requirements.

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