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This paper describes the design and development of a web-based Data Science Learning Platform (DSLP) aimed at making hands-on data science learning accessible to non computing majors with little or no programming background. The platform works as middleware between users such as students or instructors, and data science libraries (in Python or R), creating an accessible lab environment. It allows students to focus on the high-level workflow of processing and analyzing data, offering varying levels of coding support to accommodate diverse programming skills. Additionally, this paper briefly presents some sample hands-on exercises of using the DSLP to analyze data and interpret the analysis results.
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This content will become publicly available on June 26, 2026
IUSE: Engaging Non-Computing Majors in Hands-on Data Science Learning through a Web-based Learning Platform
This paper describes the design and development of a web-based Data Science Learning Platform (DSLP) aimed at making hands-on data science learning accessible to non-computing majors with little or no programming background. The platform works as middleware between users such as students or instructors, and data science libraries (in Python or R), creating an accessible lab environment. It allows students to focus on the high-level workflow of processing and analyzing data, offering varying levels of coding support to accommodate diverse programming skills. Additionally, this paper briefly presents some sample hands-on exercises of using the DSLP to analyze data and interpret the analysis results.
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- Award ID(s):
- 2336929
- PAR ID:
- 10593193
- Publisher / Repository:
- ASEE
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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