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  1. Mar 25, 2024 · With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics. Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.

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    • Traffic and Engagement Reports. One example of descriptive analytics is reporting. If your organization tracks engagement in the form of social media analytics or web traffic, you’re already using descriptive analytics.
    • Financial Statement Analysis. Another example of descriptive analytics that may be familiar to you is financial statement analysis. Financial statements are periodic reports that detail financial information about a business and, together, give a holistic view of a company’s financial health.
    • Demand Trends. Descriptive analytics can also be used to identify trends in customer preference and behavior and make assumptions about the demand for specific products or services.
    • Aggregated Survey Results. Descriptive analytics is also useful in market research. When it comes time to glean insights from survey and focus group data, descriptive analytics can help identify relationships between variables and trends.
  3. In this article, we’ll explore the importance of descriptive analytics, examine its remarkable benefits, and showcase real-world examples of how various industries use it successfully. Prepare to unlock the mysterious world of descriptive analytics and explore how it could elevate your business.

  4. Apr 5, 2023 · Descriptive analytics relies on statistical measures of distribution, central tendency, and variability. It provides an overview of varied data types, from financial statements to surveys, website traffic, and scientific data.

  5. Nov 8, 2023 · Descriptive analytics is a powerful way to understand data and make decisions. Learn the definition, see examples & benefits of descriptive analytics now.

  6. In a nutshell, these powerful data analytics techniques provide a holistic understanding of your business. First, you understand past events and uncover their root causes. Next, you use data to forecast future trends and leverage AI and ML models to get actionable recommendations.

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