Our Summary Checklist for Working with Undergraduate Students
If you are interested in recruiting for entry-level positions - and creating an ongoing pipeline of talent - you should make a university partnership with an undergraduate program a high priority. However, undergraduate students are different from master’s and doctoral-level students. Ensuring that those differences are understood and that expectations are aligned will contribute to a more successful experience for the organization, for the student, and for the associated faculty. The primary way your organization will likely engage with undergraduate students is through internships and capstone courses. Where there are differences between the two, they share many best practices that managers seeking to work with undergraduate students should consider:
/ Since internships and capstones are almost universally integrated into undergraduate data science programs, the instructors and program directors should welcome your offer to engage students in this context. This is a particularly strong way to start a conversation with a university for recruiting talent and building hiring pipelines, because they need your project, your data, and your agreement to engage to satisfy the requirements of the course offering.
/ While internships and capstones benefit the university, be aware that the “higher ranked” universities may already have a long queue of companies wanting to sponsor these projects and courses. If your organization is seeking an opportunity to engage a university specifically for the objective of hiring entry-level talent (versus research, innovation, professional education), you may want to consider reaching out to a smaller, local, undergraduate institution. In addition, hiring local talent is beneficial to the community (eat local, shop local...hire local).
/ Prior to engaging an undergraduate program, consider where the data science program is housed (e.g., Business School, Computer Science, Mathematics). Students in “data science” from the various academic units within a university will have very different strengths and weaknesses. While checking your position(s) requirements against the curriculum may be useful, a “machine learning” course in Computer Science will look very different from a “machine learning” course in a Business School.
/ Ensure that your expectations are aligned; most undergraduate students in data science will be computationally strong but have limited (or zero) domain experience.
/ Give undergraduate students opportunities to learn but also to have “wins” where they can apply and demonstrate their knowledge. Remember that this experience is part of their education.
/ Internships, applied projects, and capstone courses all require a high degree of coordination between the manager, the faculty overseeing the engagement, and the students. Successful capstone courses/applied project courses may require a mentoring conversation every week in the beginning, with more mature engagements requiring conversations once a month.
/ Factors contributing to success include well-defined, substantive content, a clear timeline, concrete deliverables, and a committed (and very patient) organizational mentor.
/ Venues for students to present the results of their work are foundational to the engagement, because they provide the student with an opportunity to develop critical “soft skills” beyond mathematics and programming.
/ Check sources like Linkedln to see if alumni from this program have been recruited into your organization in the past — and if they stayed. If your organization has not hired out of this program, what types of organizations have hired these graduates? Were they start-ups? Fortune 100 corporations? How long did they stay?
- 1. National Center for Education Statistics, https://nces.ed.gov/ipeds/ Accessed May 18,
- 2. Nate Johnson, Leonard Reidy, Mike Droll, and R.E. LeMon (2017). Program Requirements for Associate’s and Bachelor’s Degrees: A National Survey https://www. insidehighered.com/sites/default/server_files/files/Program%20Requirements% 20-%20A%20National%20Survey%281%29.pdf. Accessed May 18, 2020.
- 3. Educationdata.org. https://educationdata.org/number-of-college-graduates/#:-:text= In%20Summary%3A,%2C%20and%20finally%2C%20the%20doctorate. Accessed May 17, 2020.
- 4. Richard H. Hersh (1997). Intention and Perceptions A National Survey of Public Attitudes Toward Liberal Arts Education, Change: The Magazine of Higher Learning, 29:2, 16-23. DOI: 10.1080/00091389709603100
- 5. Nancy O’Neill (2010). Internships as a High-Impact Practice: Some Reflections on Quality, https://www.aacu.org/publications-research/periodicals/internships-high- impact-practice-some-reflections-quality. Accessed May 2, 2020.
- 6. Data Science Community. http://datascience.community/colleges Accessed June 2,
- 7. College of Charleston, http://datascience.cofc.edu/ Accessed June 2, 2020.
- 8. University of Georgia, https://www.stat.uga.edu/data-science-major Accessed June 2,
- 9. University of New Hampshire, https://ceps.unh.edu/computer-science/program/bs/ analytics-data-science-data-science-option Accessed June 2, 2020.