Roundtable on Data Science Postsecondary Education A Compilation of Meeting Highlights (2020) / Chapter Skim
Currently Skimming:
1 Introduction
1 Introduction
Pages 1-7
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 1... ...
These meeting summaries, as well as original meeting videos, are also available online.1 These meeting recaps were prepared by the 1 Watch meeting videos or download presentations at https://www.nationalacademies.org/ our-work/roundtable-on-data-science-postsecondary-education, accessed February 13, 2020.
|
From page 2... ...
. These and other paths forward could be implemented successfully alongside the following strategies: training graduate teaching assistants across a range of skills; identifying and etter supporting b faculty who are willing to experiment with and assess new approaches; improving the understanding of disciplinary needs for data science; developing methods to introduce data science to students without quantitative training; integrating standard disciplinary data sets to support data science instruction; and lessening traditional seminar teaching and single-author monograph publishing.
|
From page 3... ...
For example, doctoral students could be directly admitted into a data science program or admitted into a home department; in some cases, admission into a data science program only happens after a student arrives on campus. Several programs compel students to complete all requirements in their home departments before completing additional requirements for data science.
|
From page 4... ...
Academic institutions could stimulate successful partnerships by leveraging experiences from other disciplines; benchmarking and developing best practices; fostering continued interactions; providing firm financial support; offering resources and incentives to both
|
From page 5... ...
Rigorous approaches to these ethical questions are being implemented in research and in academic institutions through new courses on ethics in data science and through modules as part of other data science courses. In this time of innovation, making teaching materials widely and quickly available could help to expand ethical conversations in the classroom.
|
From page 6... ...
Generally accepted standards for teaching computational transparency and reproducibility in data science could be useful, as could generally accepted standards for best practices in software engineering in data science applications (see Chapter 7) . Social Good Approaches to engaging students in meaningful projects with the potential for social impact are rapidly emerging and these efforts could help to attract and retain future data scientists.
|
From page 7... ...
Mentorship programs and cohort experiences have been particularly successful in recruiting and retaining underrepresented groups for data science education. Given that academic institutions are slow to change, especially with regard to rewarding faculty involvement in activities that do not result in peerreviewed publication, partnership with industry could be a promising avenue to increase diverse participation in data science.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.