Data_Analysis_15.pptx presentation for verification

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1. Data Analysis A Presentation

2. Introduction What is data analysis?

3. Importance Why data analysis matters.

4. Types Descriptive, diagnostic, predictive, prescriptive.

5. Steps in Data Analysis Collection, cleaning, processing, visualization.

6. Data Collection Methods Surveys, sensors, digital logs.

7. Data Cleaning Removing errors, duplicates, inconsistencies.

8. Tools Excel, Python, R, SQL, Tableau.

9. Visualization Charts, dashboards, reports.

10. Business Applications Market trends, customer insights.

11. Healthcare Applications Patient data, drug effectiveness.

12. Education Applications Performance tracking, curriculum planning.

13. Challenges Data quality, privacy, big data issues.

14. Benefits Informed decisions, improved efficiency.

15. Future Trends AI, machine learning, real-time analysis.

16. Conclusion Data analysis empowers smarter decisions.