Introduction to Data Analytics and Its Importance
- 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