A Quasi-Experimental Study of Parallel Python-R Learning in Data Science Education

Implementation and Assessment in a BYOD Environment

Abstract

This study aims to verify the effectiveness of a parallel learning approach for Python and R programming languages in data science education and to develop a practical learning environment using the Windows Subsystem for Linux (WSL) and Jupyter Notebook in a Bring Your Own Device (BYOD) setting. Additionally, it measures and analyzes the educational impact of this method on specialized courses in business and healthcare. The research methodology includes implementing a curriculum for simultaneous learning of both languages in the first-year "DS Programming Introduction" course, with effectiveness measured through language comprehension tests, problem-solving ability tests, and student language preference surveys. Furthermore, the study evaluates the efficacy of the BYOD environment using the WSL and Jupyter Notebook and optimizes programming education methods for specific fields. The findings of this study are expected to contribute to the development of highly skilled data science professionals by establishing new best practices in data science education.