What are common data visualization tools?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in hThe primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem.

Common data visualization tools range from comprehensive business intelligence (BI) platforms to specialized programming libraries. The choice of tool depends on the user's technical skill, the complexity of the data, and the specific needs of the project.

Business Intelligence (BI) Platforms

These tools are user-friendly and often feature a drag-and-drop interface, making them accessible to a wide audience. They are designed for creating interactive dashboards and reports.

  • Tableau: A leading tool known for its powerful and intuitive visualization capabilities. It is highly flexible for creating complex and visually appealing charts and dashboards.

  • Microsoft Power BI: Popular for its seamless integration with other Microsoft products like Excel and Azure. It is a robust platform with a user-friendly interface that offers strong analytics features.

  • Looker: Now part of Google Cloud, Looker is a BI platform that focuses on data modeling and allows users to define metrics once and use them across multiple reports, ensuring consistency.

Spreadsheet and Office Software

These are ubiquitous and a good starting point for basic visualization tasks.

  • Microsoft Excel: Despite being a spreadsheet program, Excel has powerful built-in charting functions that are sufficient for quick analyses and simple reports.

  • Google Sheets: A free, cloud-based alternative to Excel that offers basic charting and collaboration features.

Programming Libraries

These tools require coding knowledge and are favored by data scientists and developers for creating highly customized or complex visualizations.

  • Python Libraries:

    • Matplotlib: A foundational library for creating static, publication-quality plots.

    • Seaborn: Built on top of Matplotlib, it provides a high-level interface for drawing attractive statistical graphics.

    • Plotly: Known for creating interactive and web-ready visualizations.

  • JavaScript Libraries:

    • D3.js (Data-Driven Documents): A highly flexible and powerful library that allows for the creation of custom, dynamic, and interactive data visualizations on the web.

    • Chart.js: A simple, open-source library for creating a variety of common chart types for websites.

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