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Why Exploratory Data Analysis Matters in Data Science

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Why Exploratory Data Analysis Matters in Data Science

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  1. Exploratory Data Analysis (EDA) Why EDA is Crucial for Data Scientists and Business Analysts?

  2. What is Exploratory Data Analysis? • Examining, visualizing, and summarizing data • Finds trends, patterns, and relationships • Helps understand data before decisions or modeling

  3. Why EDA is Important?

  4. How EDA Helps Data Scientists: Improves Data Quality?

  5. How EDA Helps Data Scientists: Guides Model Selection?

  6. How EDA Helps Data Scientists: Enhances Model Performance • Early pattern understanding • Helps build accurate and stable models

  7. How EDA Supports Business Analysts: Understand Trends

  8. How EDA Supports Business Analysts: Simplifies Communication • Uses charts and dashboards • Makes insights clearer for teams

  9. How EDA Supports Business Analysts: Supports Forecasting? • Identifies patterns for predictions • Helps forecast demand, risks, opportunities

  10. Business-Level Benefits ✨ Better decisions backed by insights ✨ Understanding customer and market changes ✨ Reduced risks early ✨ Strong team collaboration ✨ Efficient planning

  11. Conclusion

  12. Thank You! Visit us: https://www.demandtalk.com/

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