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Paper: National Advancement of Data Science Education #478

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commented May 23, 2019

National Advancement of Data Science Education by Anthony Suen, Alan Liang and Amal Bhatnagar-- an exploration of expanding data science education across undergraduate institutions across the country by creating an introductory data science foundation course, scalable infrastructure, and modularizing content

@deniederhut

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commented May 23, 2019

Hey! Thanks for submitting. It looks like your paper build is failing because there are no authors listed. You can see an example of how to do this in the 00_vanderwalt example paper

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commented May 24, 2019

Hi! This paper needs to build at procbuild.scipy.org before it can be reviewed. Have you had a chance to fix the authors section?

@amalbh1999

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commented May 24, 2019

Fixed! It is up on procbuild.scipy.org.

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commented May 24, 2019

Hooray! I'm glad everything is working now.

I have an email with some questions about local builds that seem to be missing from GitHub - I guess you deleted the comment? To answer the question you had, the error code you pasted probably has to do with a missing or incorrect installation of latex. The README has instructions for completing the installation, but they're a bit sparse and sort of assume you are running Linux. Let me know if you still want help with this 🙂

@awalin

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commented Jun 10, 2019

The author made strong and important points on cross-department collaboration.

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awalin approved these changes Jun 10, 2019

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@amalbh1999 I am one of the proceedings committee members, stepping in to give your paper some additional review. Thank you for this submission. I think it is nearly there. I am requesting some changes, mainly regarding references and citations, and some from proofreading of the manuscript.

  • I found it odd to encounter in-text citations only at the end of Section 3. The paper would be improved by some more citations and links.
  • For references, when DOIs are available, these should be included, per the guidelines for authors.
  • I was often reminded of a paper from SciPy2018 -- after finishing my first read of this manuscript, I looked it up and realized it is also had Anthony Suen as first author. Please cite it (including DOI).
  • For the links or references, please use references in square brackets followed by an underscore, in-text, and the same square-brackets reference in the references section. This will create a link in the PDF fore each reference. For examples, please see the 00_vanderwalt example paper, or even the 2018 submission.
Culture* 40(3): 455-483.
`*https://www.jstor.org/stable/25147356* <https://www.jstor.org/stable/25147356>`__

MacKenzie, Donald A. 1981. *Statistics in Britain: 1865-1930; The Social

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stargaser Jun 21, 2019

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not cited in the text

Eubanks, Virginia. 2018. *Automating Inequality: How High-Tech Tools
Profile, Police, and Punish the Poor*. New York: St. Martin’s.

Hacking, Ian. 1996. Normal People. Pp. 59-71 in David Olson and Nancy

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stargaser Jun 21, 2019

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Not cited in the text.

Torrance, eds., *Modes of Thought: Explorations in Culture and
Cognitions*. Cambridge: Cambridge University Press.

Hicks, Marie. 2017. *Programmed Inequality: How Britain Discarded Women

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stargaser Jun 21, 2019

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Not cited in the text.

Latour, Bruno. 1990. Technology is society made durable. *The
Sociological Review* 38(1, supplement): 103-131.

Light, Jennifer S. 1999. When computers were women. *Technology and

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stargaser Jun 21, 2019

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Not cited in the text.

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@amalbh1999

amalbh1999 Jun 25, 2019

Author

Thanks for the input on the citations. We are currently working on editing the paper on another platform and will upload the revised version to GitHub soon.

**2) Common Educational Cyber-Infrastructure**
----------------------------------------------

Implementation of a data science course in a scalable way requires

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stargaser Jun 21, 2019

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While reading this section, I kept thinking back to this paper from SciPy 2018. No wonder, the first author is also Anthony Suen! Please cite it in this section.

resource use by a large number of relatively unsophisticated users.
Making the infrastructure accessible means making it easy to use both by
instructors and students, and potentially integrating it into existing
campus Learning Management Systems (LMS), eg Canvas. For institutions

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stargaser Jun 21, 2019

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I am not familiar with Canvas. Would you please include a link or a reference?

better fit the differing needs and implementations of the data science
courses. Thus overall startup costs are expensive, and the long term
sustainability for maintaining a educational cyber-infrastructure would
come with too many question marks for many institutions faculty to make

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stargaser Jun 21, 2019

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Suggested change
come with too many question marks for many institutions faculty to make
come with too many question marks for many institutions' faculty to make

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Autograding is essential to the scalability of data science education
and alleviates substantial work for large classes at UC Berkeley, such
as *Data 8:* *Foundations of Data Science* and *Data 8X*, its massive
open online course, or MOOC, version, which see more than 1,500 students

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stargaser Jun 21, 2019

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open online course, or MOOC, version, which see more than 1,500 students
open online course, or MOOC, version, which sees more than 1,500 students
warrant an entire data science curricula, and creating a sustainable
model that supports the data science curricula.*

Implementation and integrating the new course to fit in the overall

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Implementation and integrating the new course to fit in the overall
Implementating and integrating the new course to fit in the overall

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awalin Jun 26, 2019

"Implementing"

academic curriculum is key for a seamless student experience. Because
data science serves functions in a vast array of interdisciplinary
fields of study, the ability to modify the introductory course and
tailor it to fit in with the current institution curriculum will go a

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stargaser Jun 21, 2019

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tailor it to fit in with the current institution curriculum will go a
tailor it to fit in with the current institutional curriculum will go a

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@amalbh1999

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commented Jun 26, 2019

We are in the process of editing the paper based on your comments and will upload it soon. Thanks

@awalin

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commented Jun 26, 2019

The paper needs an overall grammar and spell check but I really appreciate the idea behind this. One more thing to note is that, the curricula presented so far does not reflect much on anti-bias algorithms, and how to make Data science more inclusive. 'Bias and lack of fairness' in models is a big issue, and for a data science education that will be implemented in a broader population, this should be essential.

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