Collective Development of Large Scale Data Science Products via Modularized Assignments

Abstract

Many universities are offering data science (DS) courses to fulfill the growing demands for skilled DS practitioners. Assignments and projects are essential parts of the DS curriculum as they enable students to gain hands-on experience in real-world DS tasks. However, most current assignments and projects are lacking in at least one of two ways: 1) they do not comprehensively teach all the steps involved in the complete workflow of DS projects; 2) students work on separate problems individually or in small teams, limiting the scale and impact of their solutions. To overcome these limitations, we envision novel synergistic modular assignments where a large number of students work collectively on all the tasks required to develop a large-scale DS product. The resulting product can be continuously improved with students' contributions every semester. We report our experience with developing and deploying such an assignment in an Information Retrieval course. Through the assignment, students collectively developed a search engine for finding expert faculty specializing in a given field. This shows the utility of such assignments both for teaching useful DS skills and driving innovation and research. We share useful lessons for other instructors to adopt similar assignments for their DS courses.