Data Science for Undergraduates Opportunities and Options (2018) / Chapter Skim
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5 Evolution and Evaluation
5 Evolution and Evaluation
Pages 72-86
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From page 72... ...
Data science programs will continue to evolve within institutions and across the United States as driving factors, including student perception of career opportunities and funding for training programs from industry and government, modify student demand and associated institutional response.1 As befits a new and evolving discipline, pilot programs designed to address different needs will arise at different types of institutions. Evaluation of a program relies upon assessing student learning and gauging how well the program meets market needs.
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From page 73... ...
While other domains have started by implementing undergraduate programs and expanding upward to include master's degree programs, or with doctoral programs and expanding downward, data science has taken an unusual path to date, as many master's degree programs have begun to be offered before undergraduate or doctoral programs. This institutional structure is perhaps driven by the rapidly arising industry demand for data scientists.
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From page 74... ...
Since data science programs in general are new, there is little in terms of comprehensive data on general expectations for the undergraduates who enter them. Many programs that exist today were started as professional master's degree programs.
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From page 75... ...
It is crucial that funding organizations and curriculum developers continue to consider approaches that will best prepare all students for potential future work in data science. Other more widely accessible initiatives to attract diverse populations include efforts by Code.org to increase access to computer science in schools, especially for women and underrepresented minorities.
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From page 76... ...
Over time, that set of classes is likely to evolve as more custom data science classes are created. In some cases, an existing class from one department may be adopted as part of another department's data science curriculum but may eventually be duplicated (perhaps with some tailoring)
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From page 77... ...
While institutions consider the many pathways available for their students, they will also need to consider who will develop and teach the general education and data science classes: teaching faculty, adjunct instructors, teaching assistants, or tenure track faculty. It is imperative that academic institutions have a balanced faculty with both domain knowledge and data science expertise so as to best deliver data science education that will prepare students for the varied and multidisciplinary workforce that lies ahead of them.
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From page 78... ...
Obtaining and utilizing appropriate data at each step can help iteratively refine programs. Objectives, associated outcomes, metrics of success in reaching these outcomes, and the activities that impact these outcomes might be established at the level of a single program, at the level of an institution with multiple routes to success for data science students through various majors or minors, or at broader national scales.
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From page 79... ...
Administrative data sets are of a scale that make it possible to more precisely identify sta 3 Administrative records could include student transcripts (with student demographic data, academic performance indicators, and scholarship, graduation, and curriculum information) , enrollment statistics, budgetary plans, and personnel demographic data.
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From page 80... ...
Last, administrative data are often of higher quality than survey data. Rather than asking respondents to provide retrospective information, with obvious recall problems, the data exist within administrative systems.
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From page 81... ...
A concerted effort could be made to incor porate additional institutions that are implementing data science programs at the undergraduate and graduate levels.
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From page 82... ...
Such information can then be used to motivate further development and revision of the evolving data science curricula at the undergraduate level. Empirical evaluation is now possible because of the integration of multiple disparate sources of data at many academic institutions -- for example, the Ohio State University Ohio Education Research Center,4 which is a collaboration of six Ohio universities and four research organizations and has data on students from preschool through the workforce.
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From page 83... ...
From such data, it becomes possible to identify cohorts at various levels, to characterize networks that provide social support as well as access to role models, and to provide information and resources that could contribute to career success. These data can also illuminate both the labor market outcomes and shocks to demand that can drive outcomes for recent graduates.
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From page 84... ...
For example, many STEM areas actively recruit undergraduates to attend professional society conferences at which they offer a wide range of career option workshops and presentations. Students in data science fields may find that obtaining such information is more difficult than for their peers in various STEM disciplines with large and active professional ocieties.
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From page 85... ...
A more structured collaboration of existing professional societies that work well together might be more effective; subsocieties devoted to data science elements may develop in any of these societies. These subsocieties could be closely connected to the educational opportunities for their members, fostering a sense of community and improved professional development, while also promoting connections among practicing data scientists in other fields.
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From page 86... ...
American Economic Review 103(4)
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