Diversity Analytics: An Empirical Study of Student Demographics

This paper presents a comprehensive analysis of student diversity in a Higher Educational Institute (HEI). We collected primary data via a Google Form survey of 99 MBA students, capturing demographic variables (age group, gender, religion, caste, language, and specialization). Exploratory data analysis was performed using Python (with Pandas) and Google Sheets. Key findings include the age distribution skew (majority aged 23–25), a gender mix (males outnumbering females), and a dominant religious/caste group. We computed Simpson’s Diversity Index for each attribute to quantify heterogeneity. The diversity indices reveal moderate-to-low diversity in variables such as religion and caste, and higher diversity in language usage. Results are displayed through tables and charts. We discuss the implications of these findings, noting that homogeneous categories can limit inclusive learning, whereas more balanced categories promote creativity and problem-solving (Hong & Page, 2004; Jain et al., 2025). This study fills a gap in literature by applying quantitative analytics to campus demographics and provides HEI with baseline metrics to guide diversity initiatives.