Abstract
Many data science applications involve generating questions, acquiring data and preparing it for analysis—be it exploratory, inferential, or modeling focused—and communicating findings. Most data science curricula address each of these steps as separate units in a course or as separate courses. Open-ended term projects, on the other hand, allow students to put each of these steps into practice, sequentially and iteratively. In this paper we discuss what we mean by data science projects, why they are crucial in introductory data science courses, who works on these projects and how, when in the term they can be implemented, and where they can be shared.
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