Feature Engineering-Based Exploratory Analysis of Tracer Study Data for Graduate Employment Readiness

Abstract

This study aims to conduct an exploratory analysis of tracer study data to identify competency gaps and describe the job readiness of Computer Science graduates. The approach used integrates feature engineering-based competency gap calculations with descriptive analysis of work experience and application activity variables. The dataset used is derived from the results of a graduate tracer study survey, which was analyzed quantitatively without the application of predictive modeling algorithms or statistical hypothesis testing. The analysis results indicate that the largest competency gaps are in the Information Technology and Personal Development aspects. Furthermore, practical experience variables such as internships and projects show a stronger descriptive relationship to employment status than academic indicators alone. These findings provide an initial overview of relevant factors in evaluating graduate job readiness. This study is exploratory in nature and can serve as a basis for further research using predictive modeling approaches or more in-depth inferential analysis.