Several powerful libraries support data science in web applications, enabling developers to perform data analysis, machine learning, and visualization directly within web-based platforms. Pandas and NumPy handle data manipulation and numerical operations efficiently. For creating interactive visualizations, Plotly, Bokeh, and Altair are widely used. Scikit-learn provides essential tools for integrating machine learning models. When building web apps, Flask and Django serve as robust back-end frameworks, allowing seamless integration of data pipelines. Dash, built on top of Flask, is particularly useful for creating interactive analytical web apps with minimal coding. TensorFlow.js supports machine learning directly in the browser. These libraries collectively enable developers to bring real-time data insights and predictive features to web interfaces, enhancing user experience and decision-making capabilities. If you want to explore practical skills using these tools, consider checking out a data science course.