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DSLP: A Web-based Data Science Learning Platform to Support DS Education for Non-Computing Majors

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Published:03 March 2022Publication History

ABSTRACT

The presenters will demo a web-based Data Science Learning Platform (DSLP) that makes data science education accessible to students with limited or no programming background. The DSLP platform offers students with several benefits such as: (1) learn a web-based user interface to perform data science tasks without requiring coding, (2) explore popular Python data science libraries (e.g., Pandas, Matplotlib, Numpy, or Scikit-Learn) through real-time code exemplification to prepare them for advanced data science topics, (3) become familiar with the on-site user guide and helpful tips to make the platform easy to use, (4) write their own code within a sandbox, and (5) monitor their own progress by tracking their platform usage. The demo will walk through the steps of using the DSLP to perform various data science tasks and the participants will be able to try out the features mentioned above. The demo will also cover the design of course materials, including hands-on practices and lab assignments using the DSLP platform. The typical participants include instructors who are interested in teaching introductory-level data science to high school students or non-computing college majors with little or no programming background. Participants need to have a laptop with access to the Internet to attend the hands-on exercises workshop. The laptop should have a current web browser (e.g., Safari or Chrome) installed to access the web-based learning platform. This demo describes work supported by the National Science Foundation under Award 2021287.

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SIGCSE22V2-fp690.mp4

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  1. DSLP: A Web-based Data Science Learning Platform to Support DS Education for Non-Computing Majors

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    • Published in

      cover image ACM Conferences
      SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2
      March 2022
      254 pages
      ISBN:9781450390712
      DOI:10.1145/3478432

      Copyright © 2022 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 March 2022

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      Qualifiers

      • demonstration

      Acceptance Rates

      Overall Acceptance Rate1,595of4,542submissions,35%

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