Learn to create informative and attractive data visualizations in Python using the Matplotlib and Seaborn libraries. This course provides a practical introduction to plotting fundamentals, common chart types, and customization techniques, essential skills for any data analysis or AI engineering task. Start building foundational visualization skills today.
Prerequisites Basic Python helpful
Level:
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.