TITLE:
Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students
AUTHORS:
Sameh Shamroukh, Teray Johnson
KEYWORDS:
Python, Data Analytics, Factor Analysis, Business Analytics, Programming
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.4,
November
29,
2023
ABSTRACT: This pioneering research represents a unique and singular study conducted
within the United States, with a specific focus on non-technical graduate
students pursuing degrees in business analytics. The primary impetus behind
this study stems from the escalating demand for data-driven professionals, the
diverse academic backgrounds of students, the imperative for adaptable
pedagogical methods, the ever-evolving landscape of curriculum designs, and the
overarching commitment to fostering educational equity. To investigate these
multifaceted dynamics, we employed a data collection method that included the
distribution of an online survey on platforms such as LinkedIn. Our survey
reached and engaged 74 graduate students actively pursuing degrees in Business
Analytics within the United States. This comprehensive research is the first
and only one of its kind conducted in this context, and it serves as a vanguard
exploration into the challenges and influences that shape the learning journey
of Python among non-technical graduate Business Analytics students. The
analytical insights derived from this research underscore the pivotal role of
hands-on learning strategies, exemplified by practice exercises and
assignments. Moreover, the study highlights the positive and constructive
influence of collaboration and peer support in the process of learning Python.
These invaluable findings significantly augment the existing body of knowledge
in the field of business analytics. Furthermore, they offer an essential
resource for educators and institutions seeking to optimize the educational
experiences of non-technical students as they acquire essential Python skills.