Data Visualization

On This Page

• 15.1  Why Data Visualization Matters
• 15.2  Line Chart
• 15.3  Bar Chart
• 15.4  Pie Chart
• 15.5  Histogram
• 15.6  Scatter Plot
• 15.7  Heatmap
• 15.8  Box Plot
• 15.9  Pair Plot
• 15.10  Choosing the Right Chart
• 15.11  Practical Exercises
• 15.12  Module Quiz (MCQs)
• 15.13  Module Assignment
• 15.14  Interview Questions — Module 15
• 15.15  Student Notes Page

Module 15: Data Visualization

15.1  Why Data Visualization Matters

A chart can communicate a business insight in 2 seconds that would take 200 rows of numbers to explain. Matplotlib is Python's foundational plotting library, and Seaborn builds on top of it with more attractive, statistically-aware charts.

pip install matplotlib seaborn

import matplotlib.pyplot as plt
import seaborn as sns

All charts in this module use a sample dataset from Samantus Institute's own monthly revenue, course enrollments, and student performance records.

15.2  Line Chart

Line charts are best for showing trends over time — perfect for tracking revenue, growth, or performance month over month.

months = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug"]
revenue = [180000,195000,210000,205000,230000,250000,265000,280000]

plt.plot(months, revenue, marker="o", color="orange")
plt.title("Monthly Revenue Trend")
plt.xlabel("Month"); plt.ylabel("Revenue (Rs.)")
plt.show()

15.3  Bar Chart

Bar charts compare quantities across categories — ideal for comparing enrollment counts across different courses.

courses = ["SEO","Google Ads","Meta Ads","Web Design","AI Tools"]
enrollments = [45, 38, 42, 28, 55]

plt.bar(courses, enrollments, color=["navy","orange","blue","green","purple"])
plt.title("Student Enrollments by Course")
plt.show()

15.4  Pie Chart

Pie charts show proportions of a whole — best used with a small number of categories (5 or fewer).

plt.pie(enrollments, labels=courses, autopct="%1.1f%%")
plt.title("Course Enrollment Share (%)")
plt.show()

15.5  Histogram

Histograms show the distribution of a single numeric variable — how values are spread across ranges.

plt.hist(marks, bins=15, color="steelblue", edgecolor="white")
plt.title("Distribution of Student Marks")
plt.xlabel("Marks"); plt.ylabel("Number of Students")
plt.show()

15.6  Scatter Plot

Scatter plots reveal the relationship between two numeric variables — used to spot correlations and patterns.

plt.scatter(ages, study_hours, color="orange", edgecolor="navy")
plt.title("Age vs Weekly Study Hours")
plt.xlabel("Age"); plt.ylabel("Study Hours per Week")
plt.show()

15.7  Heatmap

Heatmaps visualize a matrix of values using color intensity — most commonly used to display correlation between multiple numeric variables at once.

corr = df[["Study_Hours","Attendance","Marks","Assignments"]].corr()
sns.heatmap(corr, annot=True, cmap="Oranges")
plt.title("Correlation Heatmap")
plt.show()

15.8  Box Plot

Box plots show the spread, median, and outliers of a numeric variable, often split by category — extremely useful for spotting unusual data points.

sns.boxplot(data=df, x="Course", y="Score")
plt.title("Score Distribution & Outliers by Course")
plt.show()

15.9  Pair Plot

A pair plot shows scatter plots between every combination of numeric columns at once — a fast way to spot multiple relationships in one view.

sns.pairplot(df, hue="Course")
plt.show()

15.10  Choosing the Right Chart

15.11  Practical Exercises

Basic (5 Questions)

1. Create a line chart showing your own 6-month sample expense data.

2. Create a bar chart comparing marks of 5 students.

3. Create a pie chart showing the % split of your daily activities (study, work, rest, etc.).

4. Create a histogram of 50 random exam scores.

5. Create a scatter plot of hours studied vs marks scored for 15 students.

Intermediate (5 Questions)

1. Add titles, axis labels, and a legend to any 2 charts from the Basic section.

2. Create a heatmap showing the correlation between 4 numeric columns of a sample dataset.

3. Create a box plot comparing exam scores across 3 different classes/sections.

4. Combine a bar chart and line chart on the same figure using two y-axes (advanced formatting).

5. Create a pair plot for a 3-column numeric dataset and describe one insight from it.

Advanced (5 Questions)

1. Build a complete 'Monthly Sales Dashboard' with 3 charts: line (trend), bar (by category), and pie (share).

2. Create a heatmap-based correlation analysis for a real dataset with at least 5 numeric columns.

3. Build a box plot comparing student performance across 4+ courses and write a 3-sentence interpretation.

4. Create a scatter plot with a trend line (using seaborn's regplot) and interpret the relationship.

5. Design a full one-page visual report combining 4 different chart types for a sample business dataset of your choice.

15.12  Module Quiz (MCQs)

15.13  Module Assignment

Assignment: Institute Performance Visual Report

• Create a line chart of monthly revenue for the last 6 months.
• Create a bar chart of enrollment counts across all courses offered.
• Create a pie chart showing course enrollment share (%).
• Create a box plot comparing student scores across at least 3 courses.
• Write a 4-5 sentence written summary interpreting all 4 charts together, as if presenting to institute management.

Expected Output: A complete 4-chart visual performance report with clear titles, labels, and a written business interpretation.

15.14  Interview Questions — Module 15

• Q: When would you use a line chart instead of a bar chart?
  A: A line chart is best when showing how a value changes continuously over time, while a bar chart is better for comparing discrete categories at a single point in time.
• Q: What does a box plot show that a histogram doesn't?
  A: A box plot clearly highlights the median, quartiles, and outliers, and makes it easy to compare distributions across multiple categories side by side, which a single histogram does not do as clearly.
• Q: Why use a heatmap for correlation analysis?
  A: A heatmap lets you visually scan relationships between many variable pairs at once, using color intensity to instantly highlight the strongest positive or negative correlations.
• Q: What is the relationship between Matplotlib and Seaborn?
  A: Seaborn is built on top of Matplotlib and provides higher-level, more visually polished statistical plotting functions, while still allowing full customization through Matplotlib underneath.

15.15  Student Notes Page

Use this space to write down key points, doubts, and your own examples from today's session.