Abstract
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.
Highlights
One of the most challenging—but most rewarding—ways to develop an undergraduate curriculum is to aim for the grand conceptual achievements of a field, stripping away the inessentials and conveying the core ideas in a way that reveals their beauty, their universality, and their contemporary relevance
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems
We refer to such an interlacing of computation, inference, and a real-world problem setting as a ‘vignette.’ In our design of a data science curriculum it was helpful to generate many such vignettes
Summary
One of the most challenging—but most rewarding—ways to develop an undergraduate curriculum is to aim for the grand conceptual achievements of a field, stripping away the inessentials and conveying the core ideas in a way that reveals their beauty, their universality, and their contemporary relevance. Our learning goals for the new curriculum were multifold, and avidly crossdisciplinary: We wanted students to understand how to formulate meaningful inferential problems, collect data relevant to those problems, build data analysis pipelines that allow problems to be solved at a range of scales, carry out analyses that are convincing, and make assertions or policy recommendations that carry weight Throughout this process we wanted students to be attentive to the social, cultural, and ethical contexts of the problems that they are formulating and aiming to solve, and we wanted to empower students to pursue their own unique perspective as data scientists. Later courses reinforce and expand the material by revisiting many of the same problems to which the students were exposed in their first course using more developed mathematical and computational tools and contextual frameworks
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