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Data Science Minor Overhauled to Be More Accessible to Non-STEM Majors

In the age of Big Data, algorithms play a major role in our daily lives, whether we’re doing a Google search, scanning Netflix, applying for credit, landing a job, or purchasing everything from dish soap to life insurance.

In 2020 Rutgers University–Newark responded to this technological sea change by creating a Data Science (DS) minor, an evolving program of study serving undergraduates across the campus that integrates coursework, community and corporate readiness—and that course of study got an overhaul in fall 2020 when SASN hired Nicole Richardson as Distinguished Professor of Professional Practice in Economics and Data Science.

Richardson, who was Senior Director of Data Science for before arriving at RU-N, built an 18-credit minor that included nine courses taught by faculty from several departments and featured guest lecturers from major companies, along with internship opportunities for students via an in-house Data Science Consultancy arm that enables undergraduates to work on contracted projects with area businesses.

Richardson is at it again, revamping the DS minor this spring to make it even more accessible and useful for non-STEM students and getting the new course selections ready for a fall 2023 rollout.

"With this redesign of the DS minor, we’re making good on our mission to expose all students, STEM and non-STEM alike, to data science and data-driven professions, and to evolve a data-literate citizenry,” said Richardson.

Her efforts, together with those of her redesign partner, Bruno Richard, a Research Associate in RU-N's Math and Computer Science department, include revisions of required courses along with seven new offerings, plus explicit student pathways that reflect different technical levels and career interests to better guide undergraduates in their completion of the DS minor. The core Everyday Data course now has two versions, introductory and advanced, as does the Ethical Issues in Data Science course, which enables the pair to introduce this important topic earlier on the sequence so non-STEM majors can be exposed to it.

With this redesign of the DS minor, we’re making good on our mission to expose all students, STEM and non-STEM alike, to data science and data-driven professions, and to evolve a data-literate citizenry.

“We want students from all across campus to have exposure to basic data science, analytics and ethical issues, whether they’re completing a DS minor, are STEM students or not,” said Richardson. “This new course progression does just that.”

To tailor the curriculum even more, Richardson and Richard created four distinct pathways within the new minor: one for students interested in taking DS courses but not declaring it as a minor, another for those pursuing data-driven professions such as journalism or digital marketing, a third for students pursuing traditional data-science roles, and a final path for the most technical those wanting to become machine-learning or data engineers.

New courses include Mathematics for Data Science I and II, Computing for Economics, Fundamentals of Data Science Models, and Models in Science and Engineering, which will accompany older offerings like Statistics and Machine Learning, Database Design and Management, and Agile iOS Design and Development.

Richardson’s decision to revamp the DS minor curriculum was informed by several things, including which students were sticking with DS courses, a survey of colleagues out in the field asking what knowledge their new hires lacked, and feedback from her own alums—students who had been part of the DS minor and/or her in-house Data Science Consultancy, the latter functioning as a lab with its own reinforcement mechanism built in.

“I’ve had interns from our DS minor in our DS Consultancy incubator program, and while managing them I’ve had to create training programs, which told me what skills they didn’t have and needed,” said Richardson. “I’ve also been staying in touch with our first DS cohort, and they tell me how well- or under-prepared they are. So, we have all these great feedback loops. Essentially, we put the DS minor out there in the world, looked at the data and have adjusted. We know exactly how to fill any voids, and we’ll continue to adjust as needed.”