Optimizing Educational Outcomes through Data

Using Python and SQL, I developed an analytical tool to evaluate student performance metrics across multiple schools. By analyzing test scores, attendance, and extracurricular participation, the tool identified key areas affecting academic outcomes.

With the help of visualization tools like Plotly and D3.js, I showcased the disparities in educational achievements across different demographics and regions. These visual insights spurred educational institutions to tailor their teaching methodologies and resource allocation more effectively.

Collaborating with educators, I implemented machine learning algorithms to predict students at risk of dropping out or underperforming, enabling early interventions.

Outcome:Boosted average student performance by 10% in pilot schools, reduced dropout rates, and facilitated personalized learning approaches.