22 Association of Research Libraries Research Library Issues 298 2019 at a fundamental level what every educated person must know about quantitative reasoning: how to effectively understand, process and interpret information, to inform decisions in their professional and personal lives and as citizens of the world in the 21st century,”7 said A. Paul Alivisatos in testimony to the US House of Representatives Science Committee in 2017. From the outset, the library was considered a key partner by virtue of their convening and collecting functions, enabling easy, broadly distributed opportunities for students to experiment with data and obtain peer consulting. Both Culler and Carson described the MSDSE experiment at UC Berkeley as transformative, and indicated that its enduring effects transcend data science and serve as a model for how faculty can come together around transdisciplinary research. Having established mechanisms for transdisciplinary practice as a new academic structure through BIDS, data science will become part of a larger engine at Berkeley. Culler observed that just as “Moore-Sloan wanted to create new career paths, UC Berkeley created new institutional paths.” Carson hopes that “the new integrative structure is a good model for other [programs], that are conventionally schools and colleges—to build core strength [in those programs] and have solid connections outward.” Might a new school or college of data science emerge at Berkeley or the other MSDSE sites? Perhaps, Carson conceded, but they would be Data 8 The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real- world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands- on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design. —“Data 8: The Foundations of Data Science,” http://data8.org/
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