Eric Van Dusen
University of California, Berkeley

Anthony Suen
University of California, Berkeley

Alan Liang
University of California, Berkeley

Amal Bhatnagar
University of California, Berkeley

Abstract

We outline a synthesis of strategies created in collaboration with 35+ colleges and universities on how to advance undergraduate data science education on a national scale. The four core pillars of this strategy include the integration of data science education across all domains, establishing adoptable and scalable cyberinfrastructure, applying data science to non-traditional domains, and incorporating ethical content into data science curricula. The paper analyzes UC Berkeley’s method of accelerating the national advancement of data science education in undergraduate institutions and examines the recent innovations in autograders for assignments which helps scale such programs. The conversation of ethical practices with data science are key to mitigate social issues arising from computing, such as incorporating anti-bias algorithms. Following these steps will form the basis of a scalable data science education system that prepares undergraduate students with analytical skills for a data-centric world.

Keywords

data science education, autograding, undergraduate institutions

DOI

10.25080/Majora-7ddc1dd1-000

Bibtex entry

Full text PDF

Copyright © 2019 Eric Van Dusen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.