SkyLimit Tech Hub: Data Science Training Center

Course 4: Data Visualization with Matplotlib & Seaborn

This course explores data visualization using Matplotlib and Seaborn, two powerful Python libraries for creating insightful charts, graphs, and plots. Designed for learners familiar with Python and Pandas, this course covers basic to advanced visualization techniques, enabling you to communicate data-driven insights effectively. Over one week, daily lessons build skills to create and customize visualizations for exploratory analysis and professional reporting.

Objective: By the end of the course, students will be able to create a variety of visualizations, including line plots, bar charts, histograms, scatter plots, and heatmaps, using Matplotlib and Seaborn, and apply these skills to real-world datasets for impactful data storytelling.

Scope: The course covers Matplotlib fundamentals, bar charts and histograms, scatter and line plots, advanced Seaborn visualizations, and practical visualization projects, equipping learners with tools to enhance data analysis and presentation.

Day 1: Introduction to Data Visualization

Introduction: Data visualization is the process of converting raw data into visual representations such as charts, graphs, and plots, making it easier to interpret complex information and communicate insights effectively. Visualizations help uncover patterns, relationships, and anomalies that might be difficult to detect through raw data alone, making them essential tools for analysis, storytelling, and decision-making.

Learning Objective: By the end of this lesson, learners will understand the role and importance of data visualization and will be able to create basic visualizations using Matplotlib, one of Python's most fundamental plotting libraries.

Scope of the Lesson: This lesson introduces the basics of Matplotlib, including creating simple line plots, customizing them with labels and titles, and saving the figures for reports or presentations. Learners will practice using Matplotlib’s core plotting functions to build foundational skills in visualization.

Background Information: Data visualization plays a crucial role in data science, business intelligence, and research by making data more accessible and understandable. Without visualization, it can be difficult to identify patterns, trends, or anomalies hidden in large datasets. Matplotlib is a widely used Python library specifically designed for creating static, animated, and interactive visualizations. It supports a variety of plot types, such as line charts, scatter plots, bar charts, histograms, and more. Some of the key functions provided by Matplotlib include plt.plot() for creating line graphs, plt.xlabel() and plt.ylabel() for labeling axes, plt.title() for adding titles, and plt.savefig() for saving the generated plots to files. Matplotlib also allows customization of plot elements like colors, markers, and line styles, enabling users to create clear, aesthetically pleasing graphics tailored to specific audiences or purposes. Effective use of visualization not only enhances analytical reports but also supports better storytelling with data.

Examples to Practice:

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [10, 15, 20, 25, 30]

# Creating a basic line plot
plt.plot(x, y)

# Adding labels and a title
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('Basic Line Plot Example')

# Saving the figure
plt.savefig('basic_line_plot.png')

# Displaying the plot
plt.show()

Explanation of the Example Code: In the example, the plt.plot(x, y) function draws a simple line connecting the points defined by the x and y lists. The plt.xlabel() and plt.ylabel() functions are used to add descriptive labels to the x-axis and y-axis, respectively. The plt.title() function adds a title to the plot, helping viewers quickly understand what the plot represents. Finally, plt.savefig('basic_line_plot.png') saves the plot as an image file, and plt.show() displays it on the screen. These basic commands form the foundation of building more complex visualizations.

Supplemental Information: Learning how to use Matplotlib is a critical first step toward mastering data visualization in Python. Once comfortable with basic plots, learners can advance to creating more sophisticated visuals like scatter plots, bar graphs, and multi-plot figures. Visualizations are a cornerstone of data science and business analytics because they facilitate understanding across technical and non-technical audiences.

Resources:

Day 2: Bar Charts and Histograms

Introduction: Bar charts and histograms are fundamental visualization tools in data analysis. Bar charts are used to compare different categories or groups, while histograms are used to visualize the distribution of numerical data. Both types of plots help in summarizing large datasets into easily interpretable graphics.

Learning Objective: By the end of this lesson, learners will be able to create and customize bar charts and histograms using Matplotlib, enhancing their ability to visualize categorical and numerical data effectively.

Scope of the Lesson: This session focuses on creating bar charts with plt.bar(), constructing histograms with plt.hist(), and applying customizations such as adjusting colors, modifying bin sizes, and setting bar widths for better visual clarity.

Background Information: Bar charts are ideal for displaying and comparing the quantities associated with different categories. Using plt.bar(), one can plot categories along the x-axis and their corresponding values along the y-axis. Bar charts make it easier to identify trends such as the most popular category or variations between different groups. Histograms, created with plt.hist(), are particularly useful for showing the frequency distribution of a continuous variable. Histograms group data into bins (intervals) and count the number of observations that fall into each bin, helping to reveal patterns such as skewness, modality, or the presence of outliers. Both bar charts and histograms allow for customization options, including color selection, adjusting bar width, changing bin numbers, and adding labels and titles for better readability. Mastering these visualizations provides analysts with versatile tools to represent both categorical and continuous data insights effectively.

Examples to Practice:

import matplotlib.pyplot as plt

# Bar chart example
categories = ['Apples', 'Bananas', 'Cherries', 'Dates']
values = [23, 17, 35, 29]

plt.bar(categories, values, color='skyblue')
plt.xlabel('Fruits')
plt.ylabel('Quantity Sold')
plt.title('Fruit Sales Comparison')
plt.show()

# Histogram example
import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=20, color='orange')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Data Distribution')
plt.show()

Explanation of the Example Code: In the bar chart example, plt.bar(categories, values) creates vertical bars representing the quantity of each fruit sold. The plot is customized with a sky-blue color, labeled axes, and a title for context. In the histogram example, random data is generated and plotted using plt.hist(data, bins=20), dividing the dataset into 20 intervals to visualize the distribution. The histogram is colored orange for better visibility. These examples demonstrate how bar charts highlight categorical comparisons while histograms reveal distribution shapes.

Supplemental Information: Choosing the correct plot type is critical for accurate data interpretation. Bar charts are better suited for categorical comparisons, whereas histograms are essential for understanding the shape and spread of continuous data. Thoughtful customization, such as choosing appropriate colors and bin sizes, can significantly enhance the clarity and impact of visualizations.

Resources:

Day 3: Scatter Plots and Line Plots

Introduction: Scatter plots and line plots are powerful tools for data exploration. Scatter plots help reveal the relationships and patterns between two continuous variables, while line plots emphasize trends over time or sequences. Both are foundational techniques for visualizing complex datasets in an intuitive manner.

Learning Objective: By the end of this lesson, learners will be able to create and customize scatter and line plots using Matplotlib, and interpret the underlying data insights from these visualizations.

Scope of the Lesson: This session includes creating scatter plots with plt.scatter(), building line plots with plt.plot(), and enhancing visualizations with annotations such as titles, axis labels, and legends.

Background Information: Scatter plots visualize the relationship between two numerical variables by plotting data points on a two-dimensional graph. Using plt.scatter(x, y), each point represents a paired observation, making it easier to detect correlations such as positive, negative, or no correlation. Line plots connect data points with lines and are ideal for illustrating trends over time or ordered sequences. Customizations like changing marker styles, adjusting line types (solid, dashed), adding titles, axis labels, and legends using plt.legend() improve readability and interpretation. These plots not only enhance exploratory data analysis but also support the communication of key findings in a visually compelling way.

Examples to Practice:

import matplotlib.pyplot as plt

# Scatter plot example
x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 3, 5]

plt.scatter(x, y, color='red', marker='o')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Simple Scatter Plot')
plt.legend(['Data Points'])
plt.show()

# Line plot example
x = [0, 1, 2, 3, 4, 5]
y = [0, 1, 4, 9, 16, 25]

plt.plot(x, y, 'b-', linewidth=2)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Simple Line Plot')
plt.legend(['Trend Line'])
plt.show()

Explanation of the Example Code: In the scatter plot example, individual points are plotted using plt.scatter(), with red circles representing each data pair. A title, labels, and a legend are added for clarity. In the line plot example, plt.plot() draws a blue line connecting the points, depicting a clear trend of increasing values. Line width is customized for better visibility. Both examples illustrate how scatter and line plots can effectively highlight relationships and patterns within the data.

Supplemental Information: Choosing between scatter plots and line plots depends on the story you aim to tell. Scatter plots excel at showing variability and potential correlations, while line plots are better for visualizing trends over continuous intervals. Adding thoughtful annotations such as legends, labels, and appropriate styling enhances the plot’s communicative power.

Resources:

Day 4: Advanced Visualization with Seaborn

Introduction: Seaborn is a high-level Python visualization library built on top of Matplotlib, designed to make statistical graphics more attractive and easier to create. It simplifies complex visualizations with concise commands and enhances plots with modern styling and themes.

Learning Objective: By the end of this lesson, learners will be able to use Seaborn to create advanced visualizations such as histograms, pair plots, and heatmaps, while applying aesthetic improvements using built-in themes and color palettes.

Scope of the Lesson: This session includes setting up Seaborn, creating advanced plots using sns.histplot(), sns.pairplot(), and sns.heatmap(), and applying various styling options to improve the visual appeal of the plots.

Background Information: Seaborn, imported as import seaborn as sns, offers a suite of functions that make generating sophisticated visualizations straightforward. The sns.histplot() function creates enhanced, customizable histograms with options for color, bin size, and kernel density estimation. sns.pairplot() automatically plots relationships between all pairs of variables in a dataset, making it an essential tool for exploratory data analysis. sns.heatmap() visualizes matrices, such as correlation tables, using a color gradient to represent values, making patterns easy to spot. Seaborn’s built-in themes (sns.set_theme()) and color palettes offer professional styling, making plots visually consistent and ready for presentations or publications.

Examples to Practice:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample dataset
df = sns.load_dataset('iris')

# Histogram example
sns.histplot(df['sepal_length'], kde=True, color='skyblue')
plt.title('Histogram of Sepal Length')
plt.show()

# Pair plot example
sns.pairplot(df, hue='species')
plt.show()

# Heatmap example
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

Explanation of the Example Code: The histogram created with sns.histplot() shows the distribution of sepal lengths and includes a kernel density estimate (KDE) for a smoother view of the data’s distribution. The sns.pairplot() function generates a grid of scatter plots to visualize relationships between all pairs of numerical features, with coloring by species to enhance interpretability. The sns.heatmap() visualizes the correlation matrix of the dataset, using colors to quickly convey strength and direction of relationships between variables.

Supplemental Information: Seaborn not only simplifies plotting but also raises the aesthetic quality of the charts. Whether you are exploring a dataset or preparing polished graphs for reports, Seaborn’s defaults offer both functionality and beauty. Consistent use of color themes and styles promotes readability and engagement with the audience.

Resources:

Day 5: Visualization Projects

Introduction: Building visualizations from real-world datasets develops practical skills in presenting data insights clearly and effectively. Visualization is not just about making charts — it's about telling a compelling story through data.

Learning Objective: By the end of this lesson, learners will be able to create multi-plot visualizations combining Matplotlib and Seaborn, annotate plots for better clarity, and design visuals that communicate key findings from real datasets.

Scope of the Lesson: This session includes loading real datasets, creating multiple plot types (bar charts, scatter plots, heatmaps), applying thoughtful annotations (titles, axis labels, legends), and using color palettes to maximize clarity and visual impact.

Background Information: Visualization projects mirror professional data reporting workflows. Typical tasks involve loading datasets (e.g., sales data, demographics), plotting distributions with sns.histplot(), exploring relationships with sns.scatterplot(), and highlighting correlations with sns.heatmap(). Proper use of titles (plt.title()), axis labels (plt.xlabel(), plt.ylabel()), and legends improves the reader’s understanding. Smart color choices, guided by Seaborn’s palettes, make insights pop. Practicing with real data prepares learners for industry-standard data storytelling.

Examples to Practice:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load a sample dataset
df = sns.load_dataset('tips')

# Scatter plot example
sns.scatterplot(x='total_bill', y='tip', hue='time', data=df)
plt.title('Tip Amount vs Total Bill')
plt.xlabel('Total Bill ($)')
plt.ylabel('Tip ($)')
plt.legend(title='Meal Time')
plt.show()

# Bar plot example
sns.barplot(x='day', y='total_bill', data=df, palette='muted')
plt.title('Average Total Bill by Day')
plt.show()

# Heatmap example
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Tips Dataset')
plt.show()

Explanation of the Example Code: The scatter plot visualizes the relationship between total_bill and tip, differentiated by meal time (Lunch or Dinner). The bar plot shows the average total bill for each day of the week, using a muted color palette for easy readability. The heatmap reveals correlations among numerical features, helping to quickly spot strong relationships or redundancy in the data.

Supplemental Information: In professional settings, good visualizations don't just display data — they clarify it. Clear titles, consistent color schemes, informative legends, and properly labeled axes are crucial. Real-world projects challenge you to think critically about which plots best convey which messages and how to refine your visuals for maximum clarity.

Resources:

Daily Quiz

Practice Lab

Select an environment to practice coding exercises.

Exercise

Download the following files to support your learning:

Grade

Day 1 Score: Not completed

Day 2 Score: Not completed

Day 3 Score: Not completed

Day 4 Score: Not completed

Day 5 Score: Not completed

Overall Average Score: Not calculated

Overall Grade: Not calculated

Generate Certificate

Click the button below to generate your certificate for completing the course.

© 2025 SkyLimit Tech Hub