Python Data Visualization: Create Impactful Visuals, Animations, and Dashboards by Pearson

How can you consolidate and simplify large amounts of information to help people understand it? Data visualization, that’s how. As data continues to grow in volume and complexity, the importance of effective visualization increases. In this course, Bruno Goncalves explores how the human visual cortex processes colors and shapes and how we can utilize these mechanisms for effective visualization using Python’s powerful visualization libraries.

Starting with two core libraries—pandas and Matplotlib—conquer the basics of Python data pre-processing and visualization before moving on to more advanced packages. Seaborn, built on top of Matplotlib, simplifies common tasks and enhances productivity. Interactive visualizations using bokeh and Plotly are also explored. You will use Jupyter Notebooks to craft visualizations.

Learning objectives
Describe how the fundamentals of human perception impact designing data visualizations.
Identify and select a strong visualization type for a specific dataset.
Use of Matplotlib and Seaborn for visualizations.
Use Bokeh and Plotly to produce interactive plots.
Generate animations with Matplotlib and Plotly.