Data Analytics is the science of examining raw data to uncover patterns, trends, relationships, and insights that support decision-making. It involves applying techniques from statistics, computer science, and domain knowledge to extract meaningful conclusions from data.

1. Presented By
Ms Subhasheni A
Assistant Professor
Department Of Computer Science
Sri Ramakrishna College Of Arts & Science
Coimbatore
Introduction to Data Analytics

2. What is Data Analytics?
 Data analytics is the process of examining, cleaning,
transforming, and interpreting data to discover
useful information, patterns, and support decision-
making.
 Goal: To convert raw data into actionable insights.

3. Importance of Data Analytics
• Drives better decision-making
• Identifies trends and patterns
• Enhances operational efficiency
• Improves customer experience
• Predicts future outcomes

4. Key Components of Data Analytics
1. Data Collection – Gathering data from various
sources
2. Data Cleaning – Removing errors or inconsistencies
3. Data Exploration – Understanding the data
structure
4. Data Analysis – Applying statistical or ML
techniques
5. Data Visualization – Communicating findings
through charts and dashboards

5. Types of Data Analytics
1. Descriptive Analytics – What happened?
2. Diagnostic Analytics – Why did it happen?
3. Predictive Analytics – What might happen next?
4. Prescriptive Analytics – What should we do about
it?

6. Tools Used in Data Analytics
• Spreadsheet Tools – Microsoft Excel, Google Sheets
• Statistical Tools – R, SAS
• Programming Languages – Python, SQL
• Data Visualization – Tableau, Power BI
• Big Data Tools – Hadoop, Spark

7. Real-World Applications
• Business – Customer segmentation, sales forecasting
• Healthcare – Patient diagnostics, disease prediction
• Finance – Fraud detection, risk analysis
• Marketing – Campaign effectiveness, behavior
analysis
• Sports – Performance tracking, game strategy

8. Data Analytics vs. Data Science
 Feature | Data Analytics | Data Science
 Focus | Insights from data | Predictive modeling
 Tools | Excel, Tableau | Python, ML, AI
 Outcome | Decision support | Data products, AI
systems
 Skill Set | Analytical, visual | Statistical,
programming

9. Challenges in Data Analytics
• Poor data quality
• Data silos and inconsistency
• Lack of skilled professionals
• Privacy and ethical concerns
• Integration with existing systems

10. The Future of Data Analytics
• Growth of AI and machine learning
• Real-time data processing
• Augmented analytics
• Democratization of data
• Increased focus on data ethics and governance

11. Conclusion
• Data analytics is essential in today’s digital world
• Helps businesses make smarter, faster decisions
• Knowing the tools and types is the first step in
becoming data literate

12. THANK YOU