<![CDATA[This study explores the application of data science to analyze and improve student learning outcomes within educational environments, responding to the increasing demand for data-driven approaches in education. The objective is to identify key performance indicators that influence learning success and to develop predictive models that support personalized academic interventions. The research applies a mixed-method approach, combining quantitative data analysis from student records and qualitative insights gathered from educational stakeholders. Machine learning algorithms and statistical models are employed to identify patterns and relationships within large datasets, helping to pinpoint factors such as attendance, engagement levels, and assessment performance that most strongly correlate with learning outcomes. Results indicate that predictive models can effectively forecast student performance, allowing educators to proactively support at risk students and tailor learning experiences to individual needs. Furthermore, the findings demonstrate that integrating data science tools into educational decision-making can improve not only academic outcomes but also institutional strategies for student success. This study concludes that data science offers substantial potential for enhancing learning environments, enabling a more responsive and personalized education system that supports each student’s unique journey towards academic achievement]]>
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