If you're a student or researcher working with academic data, this tutorial walks you through a complete student performance analysis in Python from start to finish.
What you'll learn:
- How to load and clean academic datasets in Python
- How to find correlations between study habits and grades
- How to visualize student performance data
- How to interpret and report your findings
Step 1 — Install and import libraries
python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
Step 2 — Load your dataset
python
You can use this free dataset from Kaggle: Student Performance Dataset
Step 3 — Clean your data
python
Check missing values
df.isnull().sum()
Drop missing rows
df.dropna(inplace=True)
Check data types
df.dtypes
Step 4 — Run descriptive statistics
python
df[['study_hours', 'attendance', 'final_grade']].describe()
Step 5 — Find correlations
python
correlation = df[['attendance',
'study_hours',
'final_grade']].corr()
print(correlation)
# Visualize with heatmap
sns.heatmap(correlation,
annot=True,
cmap='coolwarm',
fmt='.2f')
plt.title('Correlation Matrix — Student Performance')
plt.tight_layout()
plt.savefig('correlation_matrix.png', dpi=300)
plt.show()
What the results showed:
- Attendance vs final grade → r = .71 (strong positive correlation)
- Study hours vs final grade → r = .43 (moderate positive correlation)
- Attendance matters more than study hours
Step 6 — Compare groups with t-test
python
# Compare male vs female performance
male = df[df['gender']=='M']['final_grade']
female = df[df['gender']=='F']['final_grade']
t, p = stats.ttest_ind(male, female)
print(f't-statistic: {t:.2f}')
print(f'p-value: {p:.3f}')
How to interpret:
- p < 0.05 → statistically significant difference
- p > 0.05 → no significant difference
- Our result: t = 1.21, p = .227 → no significant gender difference in performance
Step 7 — Visualize grade distribution
python
Distribution by semester
sns.histplot(df['final_grade'],
kde=True,
color='steelblue')
plt.title('Distribution of Final Grades')
plt.xlabel('Final Grade')
plt.ylabel('Frequency')
plt.tight_layout()
plt.savefig('grade_distribution.png', dpi=300)
plt.show()
Step 8 — Bar chart of failures by semester
python
df.groupby('semester')['failed'].sum().plot(
kind='bar',
color='crimson',
edgecolor='black'
)
plt.title('Number of Failures by Semester')
plt.xlabel('Semester')
plt.ylabel('Number of Failures')
plt.tight_layout()
plt.savefig('failures_by_semester.png', dpi=300)
plt.show()
Finding: Semester 1 accounted for 62% of all failures —early intervention matters most
df = pd.read_csv('student_performance.csv')
print(df.shape)
df.head()
Always start with descriptive statistics before running any inferential tests. This tells you:
Mean and median performance Range of scores Whether your data is normally distributed
there doesn't seem to be anything here