Data Analytics

Data Analytics is the process of examining raw data to discover
patterns, trends, and insights that help in decision making.
It converts data into meaningful information.

1. What is Data Analytics?

Data Analytics involves collecting, cleaning, transforming, and analyzing data to answer questions and solve problems.

Focuses on insights and decisions
Uses statistical and computational methods
Works on structured and unstructured data

2. Types of Data Analytics

Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics

3. Descriptive Analytics

Descriptive analytics explains what happened in the past by summarizing historical data.

Examples:
• Monthly sales report
• Student result analysis
• Website traffic summary

4. Diagnostic Analytics

Diagnostic analytics focuses on why something happened by finding root causes.

Examples:
• Why did sales drop last month?
• Why did server traffic increase?

5. Predictive Analytics

Predictive analytics uses historical data to predict future outcomes.

Examples:
• Predict next month sales
• Predict student performance
• Predict customer churn

6. Prescriptive Analytics

Prescriptive analytics suggests actions to take for best possible outcomes.

Examples:
• Best pricing strategy
• Optimal inventory level
• Recommended actions for growth

7. Data Analytics Process

Data Collection
      ↓
Data Cleaning
      ↓
Data Transformation
      ↓
Data Analysis
      ↓
Data Visualization
      ↓
Decision Making

8. Data Analytics Tools

SQL
Python
R
Excel
Power BI
Tableau

9. Data Analytics vs Data Mining

Data Analytics               Data Mining
--------------------------- -----------------------------
Focus on insights            Focus on patterns
Decision-oriented            Discovery-oriented
Uses reports & dashboards    Uses algorithms

10. Applications of Data Analytics

Business intelligence
Healthcare analytics
Education performance analysis
Marketing strategy
Financial forecasting

11. Advantages of Data Analytics

Improved decision making
Better understanding of data
Predicts future trends
Increases efficiency

12. Challenges of Data Analytics

Data quality issues
Data security and privacy
Skill gap
Tool and infrastructure cost

Practice Questions

What is data analytics?
Explain types of data analytics.
Differentiate data analytics and data mining.
Explain data analytics process.
List data analytics tools.

Practice Task

Explain with examples: ✔ Descriptive analytics ✔ Predictive analytics ✔ Prescriptive analytics ✔ Data analytics process

📘 DBMS – Chapters