Prerequisites: Basic Python programming skills (variables, data types, loops, functions, importing libraries). Familiarity with NumPy and Pandas is helpful but not strictly required.

Level: Beginner

What You'll Learn

• Understand Visualization Principles

  Recognize the importance of data visualization and identify when to use different plot types.

• Create Basic Plots with Matplotlib

  Generate fundamental plots like line charts, scatter plots, bar charts, and histograms using Matplotlib.

• Customize Matplotlib Plots

  Modify plot elements such as titles, labels, colors, line styles, and axis limits.

• Utilize Seaborn for Enhanced Plots

  Leverage Seaborn to create statistically informative and aesthetically pleasing visualizations with simpler syntax.

• Visualize Distributions and Categories

  Create plots specifically designed for showing data distributions (histograms, KDEs, box plots) and categorical data (bar plots, count plots, swarm plots) using Seaborn.

• Integrate with Pandas

  Load data using Pandas DataFrames and create visualizations directly from them using both Matplotlib and Seaborn.

• Save and Share Visualizations

  Save your plots in various file formats for reports, presentations, or web use.