A Python Framework For Academic Data Analysis And Visualization

Abstract

In the era of data-driven education, academic institutions generate large volumes of information related to student performance, attendance, curriculum outcomes, and administrative operations. Efficient interpretation of this data is essential for informed decision-making and quality improvement. This paper presents a Python-based framework for academic data analysis and visualization that integrates various open-source libraries to automate data preprocessing, statistical evaluation, and visual representation. The framework employs Pandas and NumPy for data manipulation, Matplotlib and Seaborn for multi-dimensional visualization, and Scikit-learn for generating predictive insights. It enables educators and administrators to identify performance trends, analyze correlations among academic factors, and forecast student outcomes. The system provides an adaptable and scalable environment suitable for both institutional analytics and research purposes. Experimental evaluation using anonymized academic datasets demonstrates that the proposed framework enhances analytical efficiency and improves the interpretability of results through dynamic, interactive visual outputs. The study concludes that Python offers a powerful, flexible, and cost-effective ecosystem for developing intelligent academic analytics solutions.