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Data Visualization with Matplotlib & Seaborn

Create beautiful and insightful data visualizations using Matplotlib and Seaborn libraries in Python.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Transform your data using Matplotlib and Seaborn, two robust Python libraries for visualization.

  2. This course emphasizes practical experience in crafting impressive and informative visualizations.

  3. Explore a variety of visual formats, from basic line charts to intricate heatmaps and multi-plot arrangements.

  4. Master the art of conveying data insights through tailored, professional-quality charts and graphs.

  5. Enhance your skills in data communication with visually appealing and insightful representations.

CERTIFICATION:

  1. Earn a Certified Data Visualization Specialist with Python credential to showcase your expertise in creating impactful visualizations using Matplotlib and Seaborn.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the core concepts of data visualization and design methodologies.

  2. Utilize Matplotlib for generating line graphs, bar graphs, scatter plots, and additional chart types.

  3. Employ Seaborn for sophisticated visualizations, including heatmaps, pair plots, and categorical charts.

  4. Enhance visualizations with color schemes, themes, annotations, and labels to improve narrative clarity.

  5. Integrate Matplotlib and Seaborn for customized visual representation and handle large datasets for effective visual output.

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Course Curriculum

Introduction to Data Visualization
  1. What is Data Visualization?
    • The role of visualization in data analysis and storytelling.
    • Key principles of effective visualizations (clarity, simplicity, and accuracy).
  2. Introduction to Matplotlib and Seaborn
    • Overview of both libraries and their differences.
    • Installation and setup of required libraries.
Basics of Matplotlib
  1. Matplotlib Anatomy
    • Understanding figures, axes, and plots.
    • Customizing the canvas and layout.
  2. Basic Plotting
    • Creating line plots, scatter plots, and bar charts.
    • Adding labels, titles, and legends.
  3. Styling Plots
    • Modifying colors, markers, and line styles.
    • Using Matplotlib styles for consistent designs.
Intermediate Matplotlib
  1. Working with Subplots
    • Creating multiple plots in a single figure.
    • Using subplot() and subplots() for layout design.
  2. Annotations and Text
    • Highlighting points with annotations.
    • Adding text to plots for context.
  3. Saving and Exporting Plots
    • Exporting visualizations to PNG, PDF, or SVG formats.
    • Adjusting DPI and resolution for high-quality output.
Basics of Seaborn
  1. Introduction to Seaborn
    • Understanding Seaborn’s aesthetic enhancements over Matplotlib.
    • Overview of Seaborn’s built-in datasets for practice.
  2. Basic Plot Types
    • Creating line plots, scatter plots, and bar plots with Seaborn.
    • Adding titles, axes labels, and legends.
  3. Categorical Plots
    • Using stripplot(), boxplot(), violinplot(), and swarmplot().
Advanced Seaborn Visualizations
  1. Statistical Visualizations
    • Histograms and density plots with distplot() and kdeplot().
    • Pairwise relationships with pairplot() and jointplot().
  2. Heatmaps
    • Creating correlation heatmaps with annotations.
    • Styling heatmaps for better interpretation.
  3. Facet Grids
    • Visualizing subsets of data with FacetGrid.
    • Customizing layouts and color palettes.
Customization and Styling
  1. Customizing Seaborn
    • Using Seaborn themes (darkgrid, whitegrid, etc.).
    • Modifying color palettes and using custom palettes.
  2. Combining Matplotlib and Seaborn
    • Enhancing Seaborn plots with Matplotlib’s functionality.
    • Creating advanced custom plots.
Capstone Project
  1. End-to-End Data Visualization
    • Cleaning and preprocessing data.
    • Visualizing insights using a mix of Matplotlib and Seaborn plots.
    • Generating a final presentation-ready report.

Training Features

Hands-on Projects

Practical exercises with real-world datasets such as sales data, stock prices, and healthcare statistics.

Comparison of Libraries

Understand when to use Matplotlib vs. Seaborn for different visualization needs.

Customization Mastery

Create polished, publication-ready visualizations.

Integration with Data Science

Combine data preprocessing (e.g., pandas) with visualizations for streamlined workflows.

Capstone Project

Work on an end-to-end data visualization project to apply all learned concepts.

Certification

Earn a certificate of completion showcasing your expertise in Matplotlib and Seaborn.

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