Roundtable on Data Science Postsecondary Education A Compilation of Meeting Highlights (2020) / Chapter Skim
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5 Meeting #4: Alternative Mechanisms for Data Science Education
5 Meeting #4: Alternative Mechanisms for Data Science Education
Pages 46-61
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STANFORD UNIVERSITY'S CERTIFICATE PROGRAMS Jeffrey Ullman, Stanford University Ullman shared the history of Stanford University's professional certificate programs. In the 1960s, the School of Engineering broadcast recorded lectures through the Stanford Instructional Television Network (SITN)
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Ron Brachman, Cornell Tech, asked whether matriculated Stanford graduate students are eligible to participate in certificate programs. Ullman noted that while it is possible, students are prohibited from cross-counting courses.
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2 emerged, attracting a broad audience of quantitative students and producing holistically trained statisticians who have the foundational knowledge to work in an integrated data science environment. Participants enroll in eight courses and complete both a written portfolio and a two-semester statistics practicum.
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Kolaczyk noted that although it would be possible to move theory education to an online learning environment, this could create even more challenges in tracking students' results in the practicum. In response to a question from James Frew, University of California, Santa Barbara, Kolacyzk described that students work in small groups for nearly every component of the practicum, which provides good preparation for future workplace experiences.
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This integrated studio education makes up approximately one-third of the total coursework for students enrolled in 1-year programs and includes alternative educational activities such as 24-hour project sprints, weekly critique sessions with 3 For more information about these degree programs, see https://tech.cornell.edu/ programs/masters-programs/, accessed February 13, 2020.
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In response to a question from a participant, Brachman remarked that, depending on the program, some incoming students come to Cornell Tech directly after receiving their undergraduate degrees, while others enroll after some amount of work experience. McKeown asked about the level of interest from external companies to engage more than once in Cornell Tech's studio projects and whether there are any intellectual property issues with the data they share.
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He added that students will occasionally have to sign a nondisclosure agreement prior to participating in DataFest, or the company may lock up the data immediately after the competition concludes. David Ziganto, Metis, asked whether DataFest has considered using synthetic data instead to help avoid such privacy issues.
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BOOT CAMPS David Ziganto, Metis Founded in 2013, Metis offered its first boot camp5 in New York and now has locations in California, Illinois, and Washington. Ziganto explained that Metis's boot camp is the only one of its kind in the United States with endorsement from the Accrediting Council for Continuing Education and Training, though he hopes others will follow suit so as to improve the overall reputation of the boot camp model.
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And for students who are not yet prepared to meet the admission criteria to enroll in a boot camp, Metis provides guidance for skill building. FIGURE 5.2 Metis's comprehensive boot camp model of the data science pipeline.
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The p articipant added that both remedial and finishing programs serve equally important purposes, so it is important to clarify to participants what type of program is being offered when the term "boot camp" is used. Schmitt asked whether boot camps are targeted to particular sectors of industry, and Ziganto responded the boot camps focus on more sustainable data science fundamentals, while the sector-specific needs are addressed in Metis's corporate training programs.
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Catherine Cramer, New York Hall of Science Cramer explained that NYSCI is situated in Corona, Queens, a community that is largely Spanish-speaking and includes 60,000 students -- the largest school district in New York City. To support data literacy, the museum engages with local families, provides exhibits, offers public experiences, helps visitors understand new tools, organizes out-of-school programs, and hosts conferences.
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• Big Data for Little Kids -- A current workshop designed to under stand how 5- to 8-year-olds define, collect, represent, and inter pret data, as well as how their caregivers engage with them in data inquiry activities such as variation, measurement error, data aggregation, interpretation, and prediction via a "make-your-own museum exhibit." • DataDive Exhibit -- Playful and personally meaningful experiences with data that help visitors understand patterns, algorithms, and machine learning processes. Katy Börner, Indiana University Börner shared her work in defining, measuring, and improving data visualization literacy -- a combination of literacy, visual literacy, and data literacy that allows one to read, make, and explain data visualizations -- which is critical for success in our data-intensive global society.
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Even field trips to science museums can increase student interest in science, she added. Börner commented that many high schools are actively teaching visualization skills, and the global population of the Information Visualization MOOC has not demonstrated the decline in literacy skills that Hero described.
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Free museum entrance days and homework-help hours attract local families to the museum, which now has approximately 1,200 children visiting on a regular basis. Uzzo added that it is a challenge to appeal to and engage a wide age group in a single exhibit; however, many exhibits interest both adults and children when they simultaneously offer objects for children to manipulate and complex ideas for adults to ponder.
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Mishra also explained that there is a spectrum of projects that serve different purposes, and those that require low student-to-faculty ratios may be difficult to scale. She also highlighted that it can be difficult to access company data for student projects, which can be a concern because students may not be as excited by the alternative option of working with public data.
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MEETING #4 61 through facilitated group-based self-learning. Ziganto noted that there are only so many "big thinkers," and people who can make smaller changes are also essential -- what is most valuable to employers is a student with the right fundamental knowledge to be able to learn quickly and adapt to new situations.
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