Abstract
Much of the current postsecondary training in core data science fields treats ‘practice’ as something to be relegated to capstone projects or other final preparations before students leave their programs. Here we argue for a paradigm shift, placing a so-called practicum course at the center of a data science program, intentionally organized as a hybrid between an educational classroom and an industry-like environment. As a case study, we detail our experience of the past 5 years developing the Statistics Practicum in Boston University’s M.S. in Statistical Practice (MSSP) program. We describe the motivation, organization, and logistics of our practicum, as well as both successes and challenges we have faced. In particular, the challenge of fairly and effectively assessing student achievement and program impact in this novel setting is discussed.
Highlights
This vast enterprise we call data science can be said to be many things to many people
We have developed a data science program in which a so-called practicum course is placed at the center, intentionally organized as a hybrid between an educational classroom and an industry-like environment
Between midAugust and early October, students learned the essential skills for day-one consulting, while a team of three Ph.D. student mentors reviewed past consulting engagements to prepare for managing consulting projects and mentoring students
Summary
This vast enterprise we call data science can be said to be many things to many people. The other key component of the MSSP program is substantial sources of practice, coordinated and managed through the practicum, in the form of (i) semester-long external-partner projects and (ii) consulting projects for the university community. Most statistics programs include practical work to varying extents, often incorporated through mechanisms like class projects, capstone courses, and consulting courses or units. Students work on semester-long projects for MSSP partner organizations, typically Boston-based business and governmental units. All of this while progressively building a stronger and stronger foundation. In the Appendix we offer some additional brief historical context, from the perspective of statistics education
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