[REVIEW]: Course Materials for Data Science in Practice

whedon commented 3 years ago

Submitting author: @TomDonoghue (Thomas Donoghue) Repository: https://github.com/DataScienceInPractice/Site Version: v1.1 Editor: @magsol Reviewer: @KiCh84, @krother Archive: 10.5281/zenodo.6568091

:warning: JOSE reduced service mode :warning:

Due to the challenges of the COVID-19 pandemic, JOSE is currently operating in a "reduced service mode".

Status

Status badge code:

HTML: <a href="https://jose.theoj.org/papers/19ab6fe3aec0080e3c8208eaeaf7a670"><img src="https://jose.theoj.org/papers/19ab6fe3aec0080e3c8208eaeaf7a670/status.svg"></a>
Markdown: [![status](https://jose.theoj.org/papers/19ab6fe3aec0080e3c8208eaeaf7a670/status.svg)](https://jose.theoj.org/papers/19ab6fe3aec0080e3c8208eaeaf7a670)

Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@KiCh84 & @krother, please carry out your review in this issue by updating the checklist below. If you cannot edit the checklist please:

Make sure you're logged in to your GitHub account
Be sure to accept the invite at this URL: https://github.com/openjournals/jose-reviews/invitations

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @magsol know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Review checklist for @KiCh84

Conflict of interest

[x] As the reviewer I confirm that I have read the JOSE conflict of interest policy and that there are no conflicts of interest for me to review this work.

Code of Conduct

[x] I confirm that I read and will adhere to the JOSE code of conduct.

General checks

[x] Repository: Is the source for this learning module available at the repository url?
[x] License: Does the repository contain a plain-text LICENSE file with the contents of a standard license? (OSI-approved for code, Creative Commons for content)
[x] Version: v1.1
[x] Authorship: Has the submitting author (@TomDonoghue) made visible contributions to the module? Does the full list of authors seem appropriate and complete?

Documentation

[x] A statement of need: Do the authors clearly state the need for this module and who the target audience is?
[x] Installation instructions: Is there a clearly stated list of dependencies?
[x] Usage: Does the documentation explain how someone would adopt the module, and include examples of how to use it?
[x] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the module 2) Report issues or problems with the module 3) Seek support

Pedagogy / Instructional design (Work-in-progress: reviewers, please comment!)

[x] Learning objectives: Does the module make the learning objectives plainly clear? (We don't require explicitly written learning objectives; only that they be evident from content and design.)
[x] Content scope and length: Is the content substantial for learning a given topic? Is the length of the module appropriate?
[x] Pedagogy: Does the module seem easy to follow? Does it observe guidance on cognitive load? (working memory limits of 7 +/- 2 chunks of information)
[x] Content quality: Is the writing of good quality, concise, engaging? Are the code components well crafted? Does the module seem complete?
[x] Instructional design: Is the instructional design deliberate and apparent? For example, exploit worked-example effects; effective multi-media use; low extraneous cognitive load.

JOSE paper

[x] Authors: Does the paper.md file include a list of authors with their affiliations?
[x] A statement of need: Does the paper clearly state the need for this module and who the target audience is?
[x] Description: Does the paper describe the learning materials and sequence?
[x] Does it describe how it has been used in the classroom or other settings, and how someone might adopt it?
[x] Could someone else teach with this module, given the right expertise?
[x] Does the paper tell the "story" of how the authors came to develop it, or what their expertise is?
[x] References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?

Review checklist for @krother

Conflict of interest

[x] As the reviewer I confirm that I have read the JOSE conflict of interest policy and that there are no conflicts of interest for me to review this work.

Code of Conduct

[x] I confirm that I read and will adhere to the JOSE code of conduct.

General checks

[x] Repository: Is the source for this learning module available at the repository url?
[x] License: Does the repository contain a plain-text LICENSE file with the contents of a standard license? (OSI-approved for code, Creative Commons for content)
[x] Version: v1.1
[x] Authorship: Has the submitting author (@TomDonoghue) made visible contributions to the module? Does the full list of authors seem appropriate and complete?

Documentation

[x] A statement of need: Do the authors clearly state the need for this module and who the target audience is?
[x] Installation instructions: Is there a clearly stated list of dependencies?
[x] Usage: Does the documentation explain how someone would adopt the module, and include examples of how to use it?
[x] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the module 2) Report issues or problems with the module 3) Seek support

Pedagogy / Instructional design (Work-in-progress: reviewers, please comment!)

[x] Learning objectives: Does the module make the learning objectives plainly clear? (We don't require explicitly written learning objectives; only that they be evident from content and design.)
[x] Content scope and length: Is the content substantial for learning a given topic? Is the length of the module appropriate?
[x] Pedagogy: Does the module seem easy to follow? Does it observe guidance on cognitive load? (working memory limits of 7 +/- 2 chunks of information)
[x] Content quality: Is the writing of good quality, concise, engaging? Are the code components well crafted? Does the module seem complete?
[x] Instructional design: Is the instructional design deliberate and apparent? For example, exploit worked-example effects; effective multi-media use; low extraneous cognitive load.

JOSE paper

[x] Authors: Does the paper.md file include a list of authors with their affiliations?
[x] A statement of need: Does the paper clearly state the need for this module and who the target audience is?
[x] Description: Does the paper describe the learning materials and sequence?
[x] Does it describe how it has been used in the classroom or other settings, and how someone might adopt it?
[x] Could someone else teach with this module, given the right expertise?
[x] Does the paper tell the "story" of how the authors came to develop it, or what their expertise is?
[x] References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?

TomDonoghue commented 2 years ago

@magsol - thank you, that sounds like a practical and efficient way to try and wrap this up! Please let us know of any comments and suggestions for the paper / project, and I will try to address them as soon as I can!

magsol commented 2 years ago

@whedon generate pdf

whedon commented 2 years ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

magsol commented 2 years ago

@whedon check references

whedon commented 2 years ago

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.21105/jose.00032 is OK
- 10.5281/zenodo.4539666 is OK
- 10.1080/10691898.2020.1860725 is OK

MISSING DOIs

- None

INVALID DOIs

- None

magsol commented 2 years ago

@TomDonoghue Hi Tom, I'm bringing things over the finish line (they all look great!), and the last thing I need from you: the archive DOI. Once you've given me that I'll wrap things up.

TomDonoghue commented 2 years ago

Thanks @magsol for the quick checks here!

I have added Zenodo to the org / repo in order to create an archive DOI: Here's the Zenodo link: https://zenodo.org/record/6568091 Here's the DOI: 10.5281/zenodo.6568091

Note: I had to create a new release to start Zenodo tracking (or, at least, I wasn't sure how to retro-actively add a Zenodo DOI to a previous release), so there is now a 1.1 release to kickstart this, though there are no content changes to what is tagged under the 1.0 release (see release page: https://github.com/DataScienceInPractice/Site/releases). I don't think this matters in any meaningful way, just wanted to note it in case it's important to update to listing version number to 1.1 somewhere, since the project was previously tagged to 1.0.

Let me know if you need anything else from me / us!

magsol commented 2 years ago

@whedon set 10.5281/zenodo.6568091 as archive

whedon commented 2 years ago

OK. 10.5281/zenodo.6568091 is the archive.

magsol commented 2 years ago

@whedon set v1.1 as version

whedon commented 2 years ago

OK. v1.1 is the version.

magsol commented 2 years ago

(@TomDonoghue , thanks for the heads-up on the version change!)

magsol commented 2 years ago

@whedon recommend-accept

whedon commented 2 years ago

Attempting dry run of processing paper acceptance...

whedon commented 2 years ago

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.21105/jose.00032 is OK
- 10.5281/zenodo.4539666 is OK
- 10.1080/10691898.2020.1860725 is OK

MISSING DOIs

- None

INVALID DOIs

- None

whedon commented 2 years ago

:wave: @openjournals/jose-eics, this paper is ready to be accepted and published.

Check final proof :point_right: https://github.com/openjournals/jose-papers/pull/97

If the paper PDF and Crossref deposit XML look good in https://github.com/openjournals/jose-papers/pull/97, then you can now move forward with accepting the submission by compiling again with the flag deposit=true e.g.

@whedon accept deposit=true

labarba commented 2 years ago

We request that authors edit the metadata of the Zenodo deposit so title and author list match the JOSE paper. It's just cleaner that way as readers see these as part of the "same scholarly object." Could you do that? (Note that you may not want Zenodo to do automatic updates of versions with each release. Note also that Zenodo pulls every committer into the author list.)

TomDonoghue commented 2 years ago

I've updated the Zenodo record to match the title of the JOSE paper, and checked that the authors match. This should now be updated: https://zenodo.org/record/6568091

labarba commented 2 years ago

[x] for the JupyterBooks reference, please add {} around the author Executable Books Community so that it is rendered like that, and not like "Community, E. B."
[x] same thing for the Jupyter reference, it appears as "Jupyter, P."

labarba commented 2 years ago

[x] on line 59: "This set of materials were" >> was

TomDonoghue commented 2 years ago

@whedon generate pdf

whedon commented 2 years ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

TomDonoghue commented 2 years ago

@whedon generate pdf

whedon commented 2 years ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

TomDonoghue commented 2 years ago

@labarba - thanks for the detailed check, I have now fixed the reference and wording issues you noticed! Let me know if there is anything else that needs checking / fixing!

labarba commented 2 years ago

@whedon accept deposit=true

whedon commented 2 years ago

Doing it live! Attempting automated processing of paper acceptance...

whedon commented 2 years ago

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSE! 🚨🚨🚨

Here's what you must now do:

Check final PDF and Crossref metadata that was deposited :point_right: https://github.com/openjournals/jose-papers/pull/98
Wait a couple of minutes, then verify that the paper DOI resolves https://doi.org/10.21105/jose.00121
If everything looks good, then close this review issue.
Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

labarba commented 2 years ago

Congratulations, @TomDonoghue, your JOSE paper is published! 🚀

Huge thanks to our Editor: @magsol, and the Reviewers: @KiCh84, @krother — your contributions make this adventure in scholarly publishing possible 🙏

whedon commented 2 years ago

:tada::tada::tada: Congratulations on your paper acceptance! :tada::tada::tada:

If you would like to include a link to your paper from your README use the following code snippets:

Markdown:
[![DOI](https://jose.theoj.org/papers/10.21105/jose.00121/status.svg)](https://doi.org/10.21105/jose.00121)

HTML:
<a style="border-width:0" href="https://doi.org/10.21105/jose.00121">
  <img src="https://jose.theoj.org/papers/10.21105/jose.00121/status.svg" alt="DOI badge" >
</a>

reStructuredText:
.. image:: https://jose.theoj.org/papers/10.21105/jose.00121/status.svg
   :target: https://doi.org/10.21105/jose.00121

This is how it will look in your documentation:

We need your help!

Journal of Open Source Education is a community-run journal and relies upon volunteer effort. If you'd like to support us please consider doing either one (or both) of the the following:

Volunteering to review for us sometime in the future. You can add your name to the reviewer list here: http://jose.theoj.org/reviewer-signup.html
Making a small donation to support our running costs here: https://numfocus.org/donate-to-jose

voytek commented 2 years ago

🎉🎉🎉

atulkjhyt456 commented 1 year ago

When studying data science in practice, it's essential to have a comprehensive set of course materials to enhance your learning experience. Here are some common materials that can be included in a data science course:

Textbooks and Reference Books: Data science textbooks provide a structured approach to learning the fundamental concepts and techniques in the field. Some popular books include "Python for Data Analysis" by Wes McKinney, "Data Science for Business" by Foster Provost and Tom Fawcett, and "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.

Lecture Slides and Presentations: Lecture slides serve as a guide during classroom or online sessions. They typically include key concepts, explanations, examples, and illustrations that support the course material. These slides are often made available for students to review and reinforce their understanding.

Case Studies and Practical Examples: Real-world case studies and practical examples help students apply data science concepts to solve problems. These materials showcase how data science techniques can be used in various industries, such as finance, healthcare, marketing, and e-commerce.

Code Examples and Jupyter Notebooks: Code examples and Jupyter Notebooks provide hands-on experience with programming languages commonly used in data science, such as Python or R. These materials demonstrate how to implement algorithms, preprocess data, build models, and perform data visualizations.

Datasets: Access to curated datasets is crucial for practicing data science techniques. Datasets can include structured data, such as CSV files, as well as unstructured data, such as text documents or images. Datasets should cover a variety of domains to expose students to different data types and challenges.

Assignments and Exercises: Assignments and exercises allow students to apply their knowledge and reinforce concepts covered in the course. These can include coding tasks, data analysis projects, or model-building exercises that require students to practice using data science techniques.

Online Resources and Tutorials: Supplementary online resources, tutorials, and video lectures can provide additional explanations, alternative perspectives, and practical tips. Platforms like DataCamp, Coursera, and Kaggle offer a wide range of data science courses and tutorials that can complement the course materials.

It's worth noting that the specific materials may vary depending on the institution, course structure, and the instructor's preferences. It's always beneficial to have a mix of theoretical content, practical applications, and real-world examples to ensure a well-rounded understanding of data science in practice.

Data Science Classes in Pune

Data Science Course in Pune

Data Science Training in Pune

openjournals / jose-reviews