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Visualizing your data is one of the most critical steps in Data Science and Machine Learning. It helps you understand distributions, detect anomalies, identify correlations, and extract actionable insights. This guide walks through the three main types of data analysis using Matplotlib and Seaborn, using the student performance dataset (student_dataset.csv):
  1. Univariate Analysis: Analyzing one variable at a time.
  2. Bivariate Analysis: Analyzing relationships between two variables.
  3. Multivariate Analysis: Analyzing interactions between three or more variables.

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1. Setup and Loading Data

First, import the required libraries and load the student performance dataset: 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Set global Seaborn style
sns.set_theme(style="whitegrid", palette="muted")

# Load the dataset
df = pd.read_csv('public/data/student_dataset.csv')

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2. Univariate Analysis (Analyzing Single Variables)

Univariate analysis inspects the frequency, distribution, and spread of a single column without considering its relationship to other columns.

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A. Numerical Columns (Histograms & KDE)

To visualize the spread of continuous values like study_hours or exam_score, use a histogram with a density curve: 
# Distribution of Study Hours
plt.figure(figsize=(7, 4))
sns.histplot(data=df, x="study_hours", kde=True, color="teal")
plt.title("Univariate Analysis: Distribution of Weekly Study Hours")
plt.xlabel("Study Hours per Week")
plt.ylabel("Number of Students")
plt.show()

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B. Categorical Columns (Count Plots)

To visualize the frequency of discrete labels like placement_status, use a count plot: 
# Count of Placed vs. Not Placed Students
plt.figure(figsize=(5, 4))
sns.countplot(data=df, x="placement_status", palette="pastel")
plt.title("Univariate Analysis: Student Placement Counts")
plt.xlabel("Placement Status")
plt.ylabel("Student Count")
plt.show()

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3. Bivariate Analysis (Analyzing Two Variables)

Bivariate analysis studies how two variables interact with each other.

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A. Numerical vs. Numerical (Scatter Plots)

To find correlations or associations between two continuous variables (e.g., study_hours vs. exam_score), use a scatter plot: 
# Relationship between study hours and exam scores
plt.figure(figsize=(7, 4))
sns.scatterplot(data=df, x="study_hours", y="exam_score", alpha=0.6, color="purple")
plt.title("Bivariate Analysis: Study Hours vs. Exam Scores")
plt.xlabel("Weekly Study Hours")
plt.ylabel("Exam Score (out of 100)")
plt.show()

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B. Numerical vs. Categorical (Box Plots / Violin Plots)

To compare distributions of a continuous variable (like exam_score) across different categories (like placement_status), use a box plot or a violin plot: 
# Comparing exam scores across placement categories
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Box Plot (shows quartiles and outliers)
sns.boxplot(data=df, x="placement_status", y="exam_score", ax=axes[0], palette="Set2")
axes[0].set_title("Exam Scores by Placement (Box Plot)")
axes[0].set_xlabel("Placement Status")
axes[0].set_ylabel("Exam Score")

# Violin Plot (shows density shape of distributions)
sns.violinplot(data=df, x="placement_status", y="exam_score", ax=axes[1], palette="Set2")
axes[1].set_title("Exam Scores by Placement (Violin Plot)")
axes[1].set_xlabel("Placement Status")
axes[1].set_ylabel("Exam Score")

plt.tight_layout()
plt.show()

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C. Categorical vs. Categorical (Grouped Count Plots)

To study the overlap between two categories (e.g., how sleep habits affect placement), group a category using the hue parameter: 
# First, create a categorical 'sleep_category' column
df['sleep_category'] = np.where(df['sleep_hours'] >= 7, 'Healthy', 'Sleep Deprived')

# Plot grouped counts
plt.figure(figsize=(7, 4))
sns.countplot(data=df, x="sleep_category", hue="placement_status", palette="coolwarm")
plt.title("Bivariate Analysis: Sleep Quality vs. Placement Status")
plt.xlabel("Sleep Category")
plt.ylabel("Number of Students")
plt.legend(title="Placement")
plt.show()

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4. Multivariate Analysis (Analyzing Multiple Variables)

Multivariate analysis looks at how three or more variables interact. It is highly useful for spotting complex patterns, trends, and identifying correlations across the entire dataset.

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A. Scatter Plots with Color & Size Mapping

You can map a third variable to the color (hue) and a fourth variable to the size (size) of dots in a scatter plot: 
# Analyzing Study Hours vs. Exam Score, grouped by Placement, sized by Attendance
plt.figure(figsize=(9, 5))
sns.scatterplot(
    data=df.sample(n=1000, random_state=42),  # Sample 1000 rows for cleaner visual display
    x="study_hours", 
    y="exam_score", 
    hue="placement_status", 
    size="attendance", 
    sizes=(20, 200),
    alpha=0.7, 
    palette="RdYlGn"
)
plt.title("Multivariate Analysis: Study Hours vs. Exam Scores (Sized by Attendance)")
plt.xlabel("Weekly Study Hours")
plt.ylabel("Exam Score")
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')  # Place legend outside the chart
plt.tight_layout()
plt.show()

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B. Correlation Heatmaps

To evaluate all linear relationships across the entire dataset simultaneously, generate a correlation heatmap: 
# Select only numerical features
numeric_df = df.select_dtypes(include=["number"])

# Calculate Pearson correlation matrix
corr_matrix = numeric_df.corr()

# Plot Heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f", vmin=-1, vmax=1, linewidths=0.5)
plt.title("Multivariate Analysis: Correlation Heatmap of Student Metrics")
plt.show()

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Practice and Next Steps

Before moving to the next section, make sure to practice your overall Data Visualization skills using the interactive notebook:

Data Visualization Exercise

Practice your skills using the interactive notebook.💻 VS Code | 🚀 Colab | 📥 Download

Course Summary

Review what you have learned and discover advanced learning paths.