Advanced visualization techniques for data science
Data visualization is the graphical representation of data and information. Effective visualizations can reveal patterns, trends, and insights that might not be apparent from raw data alone. While Matplotlib provides the foundation for plotting in Python, libraries like Seaborn build on Matplotlib to provide higher-level interfaces for statistical graphics. Advanced visualization techniques include: multi-panel figures, interactive visualizations, geographic mapping, and specialized plots for specific data types. Good visualization practice involves choosing the right plot type for the data, using color effectively, providing clear labels and annotations, and avoiding misleading representations. Understanding data visualization is crucial for exploratory data analysis, communicating results to stakeholders, and creating compelling data stories. Python's visualization ecosystem continues to grow with libraries like Plotly for interactive visualizations and Altair for declarative statistical visualization.