Enhancing The University of Arkansas\u27 Operations Through Data Science

Abstract

As universities navigate financial constraints and resource allocation challenges, data driven financial analysis has become increasingly important. Universities employ various methods to assess financial efficiency, predict future expenditures, and optimize student credit hour distribution. However, the approaches to financial analysis vary widely, with some institutions leveraging advanced predictive modeling and business intelligence tools, while others rely on traditional budgeting techniques and manual forecasting. This thesis examines how the University of Arkansas\u27 (“Uark”) financial analysis methods compare to those of other institutions and alternative data-driven approaches. Using four years of financial and student credit hour data, this study evaluates cost trends and student credit hour patterns in UArk’s financial management framework. Additionally, a comparative analysis is conducted to assess the strengths and limitations of different financial analysis methodologies. Through this comparison, this research identifies best practices in data-driven financial planning and provides insights into how the University of Arkansas can improve its utilization of data science. The findings contribute to the ongoing discourse on data science applications in institutional decision-making, offering a framework for universities seeking to enhance their budgeting, forecasting, and resource allocation processes