The Short Version

An exploratory data analysis of 1,000 students’ exam scores across math, reading, and writing. The goal was to understand how demographic and socioeconomic factors — gender, race/ethnicity, parental education, lunch type, and test prep — influence academic performance. It surfaces clear, data-backed patterns about what separates high-scoring students from low-scoring ones.

Problem

Which student background factors most strongly predict exam scores? Do gender gaps exist across subjects? Does completing a test preparation course actually make a measurable difference? This project started from curiosity about whether simple demographic variables could explain meaningful variation in student outcomes.

Approach

The dataset (sourced from Kaggle) contains 8 columns and no missing values — clean enough to jump straight into analysis. The approach used:

• Pie charts and sunburst charts (Plotly) to visualize the composition and intersections of categorical variables like race, gender, lunch type, and parental education.
• Histograms and distribution plots (Plotly) to compare score distributions by group, reporting both mean and mode per segment.
• Grouped bar charts to compare average scores across demographic categories.
• A correlation heatmap (Seaborn) to measure how math, reading, writing, and total scores relate to one another.
• A derived total score column (sum of all three subjects) was engineered for aggregate comparisons.

Result

Several clear patterns emerged:

• Gender: Males outperform females in math; females outperform males in both reading and writing, and earn higher total scores overall.
• Race/Ethnicity: Group E achieves the highest average total score and is the only group where math scores exceed reading/writing averages.
• Parental Education: Students whose parents hold a master’s degree score higher across all subjects.
• Lunch Type: Students on a standard lunch plan consistently outscore those on free/reduced lunch — a proxy for socioeconomic status.
• Test Preparation: Completing the test prep course is associated with higher scores across all three subjects.
• Score Correlations: Reading and writing scores are strongly correlated with each other and together drive total score more than math does.

What I Learned

Socioeconomic signals (lunch type, parental education) appear at least as influential on outcomes as direct preparation factors (test prep course). Visualizing multi-variable interactions through sunburst charts made it easier to spot compound effects — e.g., how gender splits differ across race groups at each parental education level — that flat bar charts would have missed.

Links

Kaggle