Abstract
Data scientists rely on many technical skills and the ability to reason about data to solve problems. As educators grapple with how to prepare students in this new field, they are faced with identifying both what a student must know and what a student should be able to do by the end of their data science education, and also how to collect evidence of those abilities. We present a way to unite and coordinate individual efforts toward training well-rounded data scientists: a data science portfolio that highlights strong communication. This structuring of classroom assignments provides a way to evaluate students' mastery of material in each class and also allows for a student to build a professional portfolio that remains valuable after the class is over.Data science portfolio pieces broadly include written and visual assignments that give students practice crafting data-driven arguments and narratives for a variety of audiences. This flexible nature of the portfolio gives students a way to demonstrate their abilities in an inclusive, âchoose your own adventureâ way.As students refine and share their portfolios with others throughout a course or program, they can see their own growth and receive feedback from instructors, peers, and the broader data science community.We provide examples of and guidance for how to implement a data science portfolio approach in single courses and wider data science programs.
Highlights
Developing as a data scientist and being secure in this identity comes from practicing one's craft and gaining confidence in one's skills and ability to reason about a datarelated question
We present a way to unite and coordinate individual efforts toward training wellrounded data scientists: a data science portfolio that highlights strong communication
The remainder of this article is structured as follows: We first describe the strengths of a data science portfolio and its benefits for students, we describe the details of portfolio creation based on our experiences in the classroom, we provide structural advice for departments and programs and implementation advice for individual instructors, and we close with a discussion of related efforts and conclusions about how our approach fits in and augments the current state of data science education
Summary
Developing as a data scientist and being secure in this identity comes from practicing one's craft and gaining confidence in one's skills and ability to reason about a datarelated question. Data science takes a multifaceted approach to exploring, analyzing, and solving problems with data, and we believe that students should provide multifaceted evidence of their mastery of these skills and their ability to reason with data. Portfolios, a collection of work that provides evidence of a person's talents and skills, have commonly been used professionally in the arts (Scolere, 2019) and have been used in a general education setting (Arter & Spandel, 2005; Burnett & Williams, 2009; Carleton College, n.d.; Crump, 2019) to collect and share student work. Code portfolios (e.g., sharing projects on GitHub) have become a common add-on to a job application as evidence of various computational skills (Craig et al, 2018; Marlow & Dabbish, 2013)
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