Data Science for Undergraduates Opportunities and Options (2018) / Chapter Skim
Currently Skimming:
4 Starting a Data Science Program
4 Starting a Data Science Program
Pages 60-71
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 60... ...
However, data science programs pose particular challenges owing to their interdisciplinary nature, the broad set of topics they encompass, and the acquisition of data and large-scale computational infrastructure they require. Thus, launching a new undergraduate program in data science may be a significant undertaking in many institutions.
|
From page 61... ...
ENSURING BROAD PARTICIPATION According to the South Big Data Innovation Hub's Keeping Data Science Broad, "the variety of perspectives such diversity [in terms of race, gender, religious affiliation, socioeconomic status, ethnicity, and first-generation status] provides is as essential as that provided by the transdisciplinary nature of data science for innovation and growth of the field" (RawlingsGoss, 2018, p.
|
From page 62... ...
Some of the introductory data science courses described in this report have made inclusion and broad participation a central goal, shaping pedagogy, technical infrastructure, and staffing. Some notable steps include the following: • Designing the material to avoid the need for mathematics, statis tics, or programming prerequisites beyond that required for entry to the academic institution, thereby avoiding demographic skews that such prerequisites might induce.
|
From page 63... ...
. Harvey Mudd faculty teach problem solving using real-world examples, offer four unique styles of one introductory computer science course based on student knowledge and interest, require students to work together in completing homework assignments, and actively encourage students to enroll in a subsequent computer science course (Williams, 2017)
|
From page 64... ...
agrees that the notion of the pipeline does not extend far enough, as underrepresented minority students face heightened entry and retention barriers. In combination with the recommendations from the Joint Working Group, increasing teacher assistant training, awareness for advising staff, communication between students and faculty, and partnerships with high school teachers could help postsecondary data science programs retain a more diverse student body (Varma, 2006)
|
From page 65... ...
Initially, at least, creative ways of involving faculty from multiple departments is likely to be necessary, so that they can learn from each other and so that students get the broad view of data science that the committee envisions. However, cross-departmental or institutional collaboration to develop data science programs may prove easier in theory than in practice.
|
From page 66... ...
Several specific hurdles to launching and sustaining data science programs have been encountered and to some extent overcome at various academic institutions. Some of these challenges are associated with growing pains of starting up any new program that is in high demand: • Overcoming initial resistance.
|
From page 67... ...
have put forth some suggestions on creating institutional change in data science, including establishing a neutral space for students and faculty to gather, providing access to professional data scientists and research software engineers who can assist and serve as role models, developing a data science consulting capability, considering the scalability of data science educational initiatives, encouraging software and data openness and reuse, and involving a wide range of people in data-intensive discovery. Another challenge will be that many fields involved with data science are themselves experiencing rapid change and evolution.
|
From page 68... ...
While several of the larger research universities retain high-performance computing and large server facilities, most universities and colleges are in the process of transitioning their computing and storage to cloud service providers that provide students reliable access to their data and the computational resources to run algorithms against the data. Thus, the cloud has played and will continue to play an important role in transforming data science education.
|
From page 69... ...
It is important to note that, as the field of data science continues to evolve at a rapid pace, it will often be necessary to reevaluate the types of careers utilizing data science as well as the data science skill sets necessary to achieve success in those careers. FACULTY RESOURCES Mirroring the variety of pathways for data science education discussed in Chapter 3, there are a number of ways in which data science courses may be taught.
|
From page 70... ...
. Some academic institutions have developed their own focused data science education programs for their faculty; for example, the University of California, Berkeley, offers summer workshops on the pedagogy and practice of data science to engage faculty across the university.4 Perhaps most challenging is retaining faculty in data science programs.
|
From page 71... ...
2006. Making computer science minority-friendly: Computer science programs neglect diverse student needs.
|
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.