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    • Microsoft Excel. Excel at a glance: Type of tool: Spreadsheet software. Availability: Commercial. Mostly used for: Data wrangling and reporting. Pros: Widely-used, with lots of useful functions and plug-ins.
    • Python. Python at a glance: Type of tool: Programming language. Availability: Open-source, with thousands of free libraries. Used for: Everything from data scraping to analysis and reporting.
    • R. R at a glance: Type of tool: Programming language. Availability: Open-source. Mostly used for: Statistical analysis and data mining. Pros: Platform independent, highly compatible, lots of packages.
    • Jupyter Notebook. Jupyter Notebook at a glance: Type of tool: Interactive authoring software. Availability: Open-source. Mostly used for: Sharing code, creating tutorials, presenting work.
  1. Learn about the top 9 data analytics tools for data analysts, such as Python, R, SQL, and Julia. Find out how to choose the best tools based on business needs, scalability, and ease of use.

  2. Tableau is a visual analytics platform that helps people see, understand and act on data. It offers cloud, on-premises, and Salesforce deployments, integrated AI/ML capabilities, data management, and intuitive products.

    • RapidMiner. Primary use: Data mining. RapidMiner is a comprehensive package for data mining and model development. This platform allows professionals to work with data at many stages, including preparation, visualization, and review.
    • Orange. Primary use: Data mining. Orange is a package renowned for data visualization and analysis, especially appreciated for its user-friendly, color-coordinated interface.
    • KNIME. Primary use: Data mining. KNIME, short for KoNstanz Information MinEr, is a free and open-source data cleaning and analysis tool that makes data mining accessible even if you are a beginner.
    • Tableau. Primary use: Data visualization and business intelligence. Tableau stands out as a leading data visualization software, widely utilized in business analytics and intelligence.
  3. Learn about the essential data science tools for beginners and data practitioners to efficiently ingest, process, analyze, visualize, and model the data. Discover new and established tools for Python, open-source, proprietary, and AI-powered data analysis.

  4. Mar 26, 2024 · Compare the features, prices and ratings of the top data analytics software for small businesses. Learn what data analytics is, how it can help your business and what types of tools are available.

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  6. Learn about 15 data analytics tools, their features, pros, and cons. Compare Tableau, Power BI, Python, R, IBM Watson, SAS, Hadoop, Spark, KNIME, RapidMiner, Alteryx, QlikView, Orange, and SAP Analytics Cloud.

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