What advanced data visualization techniques can you use?

2 Network Visualization

Network visualization is the technique of displaying data that has a relational or structural component, such as connections, links, or hierarchies. Network visualization can help you explore, understand, and communicate the complex and dynamic interactions between entities in your data. For example, you can use network visualization to model social networks, web graphs, genealogy trees, or organizational charts. To create network visualizations, you can use tools and libraries such as Gephi, Cytoscape, NetworkX, or Vis.js.

4 Time Series Visualization

Time series visualization is the technique of displaying data that has a temporal or sequential component, such as dates, times, or events. Time series visualization can help you examine, compare, and forecast the changes and trends in your data over time. For example, you can use time series visualization to monitor stock prices, sales performance, weather patterns, or user behavior. To create time series visualizations, you can use tools and libraries such as Highcharts, Matplotlib, Bokeh, or Plotly.

5 Dimensionality Reduction Visualization

Dimensionality reduction visualization is the technique of displaying data that has a high-dimensional or multivariate component, such as features, attributes, or variables. Dimensionality reduction visualization can help you reduce the complexity and noise in your data, and reveal the underlying structure and relationships among the data points. For example, you can use dimensionality reduction visualization to cluster, classify, or segment your data. To create dimensionality reduction visualizations, you can use tools and libraries such as Scikit-learn, Tensorflow, Radviz, or t-SNE.