"Big Data, Data Science and Next Steps for the Undergraduate Curriculum"
Nicholas J. Horton, Amherst College; & Johanna Hardin, Pomona College
Papers by Nolan and Temple Lang ("Computing in the Statistics Curricula", TAS 2010) and Gould ("Statistics and the Modern Student", ISR 2010) challenged the statistics community to consider how new computational approaches and novel datasets should be incorporated into our courses and programs. We know that statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world, but there is considerable uncertainty about how to accomplish this. The American Statistical Association convened a working group to consider these and related issues as part of an overhaul of their guidelines from 2000 for undergraduate programs in statistics. This group has been working over the past 9 months to better understand the challenges and opportunities in this area. As statistics educators, we play a key role in helping to prepare the next generation of statisticians and data scientists. Inaction is not an option, since statistics could be viewed as obsolete if these new areas and applications are not embraced.
What have we learned so far? What has changed since 2000? A key area we see is the expanded role of data science and computation in the undergraduate statistics curriculum. In a world increasingly awash in ever more complex data, there is additional demand for graduates who are able to extract actionable information from data. This requires the ability to grapple with data or think computationally in a nimble fashion.
In this session, members of the workgroup will briefly describe the background and existing guidelines, then propose several different ways under consideration to further develop the capacity for students to use big data to answer important questions (e.g. "think with data" in the sense of Diane Lambert from Google). The presentation will be relatively brief, with the majority of time devoted to discussions based on feedback from the audience facilitated by multiple clicker questions. This will include thoughts about specific implementations, barriers, and ways to provide faculty development opportunities. As an example, a question might ask whether participants felt it feasible to integrate learning objectives related to access to large (hundreds of gigabyte) databases in a regression (second course). Volunteers endorsing "strongly disagree" would be asked to jump in to describe why, along with those who said "strongly agree".
The session would be helpful in eliciting feedback from the community as the workgroup prepares its draft recommendations for the JSM 2014, and the timing is ideal as we refine our proposals.
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