about data analytics

1. Data Analyst
• Role, Skills, and Career Path

2. Introduction
• A Data Analyst collects, processes, and analyzes data to help
organizations make better decisions. They turn raw data into
meaningful insights.

3. Importance of Data Analysis
• • Helps businesses make informed decisions
• • Identifies trends and opportunities
• • Improves efficiency and performance
• • Reduces risks through predictive analysis

4. Types of Data Analysis
• • Descriptive – What happened?
• • Diagnostic – Why did it happen?
• • Predictive – What will happen?
• • Prescriptive – What should we do?

5. Key Responsibilities
• • Collecting and cleaning data
• • Performing statistical analysis
• • Creating visualizations and dashboards
• • Presenting insights to stakeholders

6. Technical Skills
• • Excel for quick analysis
• • SQL for querying databases
• • Python/R for advanced analysis
• • Tableau/Power BI for visualization

7. Soft Skills
• • Communication – Explain results clearly
• • Problem-solving – Find solutions from data
• • Critical Thinking – Interpret data correctly
• • Collaboration – Work with teams effectively

8. Popular Tools
• • Excel, Google Sheets
• • SQL (MySQL, PostgreSQL)
• • Tableau, Power BI
• • Python, R
• • Jupyter Notebook, Google Data Studio

9. Data Collection
• • Sources: Databases, APIs, surveys, logs
• • Methods: Web scraping, manual entry, automated pipelines
• • Ensures accurate and reliable data for analysis

10. Data Cleaning & Preparation
• • Handling missing values
• • Removing duplicates
• • Standardizing formats
• • Ensuring data quality for accurate results

11. Exploratory Data Analysis (EDA)
• • Summarizing datasets
• • Identifying patterns and correlations
• • Spotting outliers and anomalies
• • Using graphs and statistics for insights

12. Data Visualization
• • Charts: Bar, Line, Pie, Histogram
• • Dashboards: Interactive reports
• • Storytelling with data: Making results easy to understand

13. Case Study Example
• E-commerce company used data analysis to:
• • Identify most popular products
• • Reduce delivery delays
• • Improve customer satisfaction
• Result: 20% sales growth in 6 months

14. Data Analyst vs Others
• • Data Analyst: Focus on insights from data
• • Data Scientist: Builds models, uses ML
• • Data Engineer: Manages data infrastructure

15. Career Path
• • Entry-level: Junior Analyst
• • Mid-level: Data Analyst / Business Analyst
• • Senior-level: Senior Analyst / Analytics Manager
• • Specialist: Data Scientist, Data Engineer

16. Industries Hiring
• • Finance & Banking
• • Healthcare
• • E-commerce & Retail
• • IT & Software
• • Marketing & Advertising
• • Government & NGOs

17. Challenges
• • Poor data quality
• • Data privacy & security issues
• • Handling large datasets (Big Data)
• • Communicating complex results clearly

18. Future Trends
• • Artificial Intelligence in analytics
• • Automation of data cleaning
• • Real-time data analysis
• • Cloud-based analytics tools

19. Tips for Success
• • Learn continuously (new tools & skills)
• • Build strong portfolios with projects
• • Network with professionals
• • Practice problem-solving with real data

20. Conclusion
• Data Analysts are vital in today’s data-driven world. They turn raw
data into knowledge, driving smarter decisions and innovation.
• Q&A