This course will provide an in-depth overview of data visualization using Python. Students will gain a comprehensive understanding of how to create powerful visualizations to communicate data insights effectively. 

Section 1: Introduction to Visualization

• Principles of effective data visualization
• Types of visualizations and their use cases
• Overview of popular Python libraries for data visualization

Section 2: Plotting with Matplotlib

• Setting up a plotting environment with Matplotlib
• Creating basic line plots, scatter plots, and bar charts
• Customizing plot aesthetics such as color, font, and labels
• Creating advanced plot types such as heatmaps and 3D plots

Section 3: Data Visualization with Seaborn

• Introduction to Seaborn and its advantages over Matplotlib
• Creating attractive statistical visualizations such as boxplots, violin plots, and swarmplots
• Customizing plot aesthetics in Seaborn

Section 4: Interactive Visualizations with Bokeh

• Introduction to Bokeh and its advantages over static visualizations
• Creating interactive visualizations such as scatter plots, line plots, and heatmaps
• Customizing plot aesthetics in Bokeh

Section 5: Interactive Visualization with Plotly

• Introduction to Plotly and its advantages over static visualizations
• Creating interactive visualizations such as scatter plots, line plots, and heatmaps
• Customizing plot aesthetics in Plotly

Section 6: Best Practices in Data Visualization

• Choosing the appropriate visualization for your data
• Tips for creating visually appealing and informative visualizations
• Examples of data visualizations used in industry and academia

Section 7: Real-world Applications of Data Visualization with Python

• Using Python to visualize data from a variety of sources, such as CSV files and SQL databases
• Hands-on exercises and projects to apply the knowledge learned in the course

By the end of this course, students will have a solid understanding of how to use Python to create visually appealing and interactive visualizations that effectively communicate insights. They will also be able to apply best practices in data visualization and understand how to use visualization in real-world applications.