Introduction to Data Analysis explores methods for collecting, cleaning, visualizing, and interpreting data to uncover patterns and support decision-making. Learners gain foundational skills in statistics, analytical thinking, and tools like Excel, Python

Data Analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. An Introduction to Data Analysis course or module provides the foundational knowledge and skills needed to understand and interpret data effectively.

In this course, learners are introduced to key concepts such as data types, data collection methods, data visualization, descriptive and inferential statistics, and the use of analytical tools like Excel, Python, R, or SQL. Through hands-on exercises and real-world examples, participants learn how to summarize datasets, identify patterns and trends, and communicate findings clearly through charts and reports.

By the end of the course, students gain the ability to make data-driven decisions and build a strong base for more advanced topics in data science and machine learning.

1. Introduction to Data Analysis by Nusrat Gulbarga

2. Tabular Formats CSV, TSV Lightweight, plain text. Excel Rich features, charts, macros. Use Cases Financial data, reports.

3. Hierarchical & Binary Formats JSON Human & machine-readable. XML Tag-based structure. Protocol Buffers Compact, fast. Use Cases APIs, config files, messaging.

4. Document, Image, Media Formats PDF, DOCX Reporting, documentation. JPEG, PNG Visual content. MP3, MP4 Audio/video analytics.

5. Database-Specific Formats SQL Structured queries, transactions. NoSQL Schema-less, flexible. Use Cases Web apps, IoT, real-time data.

6. Summary & Quiz Recap Data Types, Sources, Formats. 1 Quiz 5 quick questions. 2 Reinforce Learning objectives. 3

7. Tools Overview & Demo 1 SQL Workbench Database management. 2 Excel Data manipulation. 3 Postman API testing. 4 Jupyter Notebook Data format handling.

8. Real-World Use Cases Business Intelligence Dashboards. E-commerce Recommendation engines. Healthcare Patient data analysis. IoT Device monitoring.

9. Wrap-Up & Next Steps 1 Tools/Formats Learn what to use. 2 Data Sources Know where data comes from. 3 Data Structures Understand them.

10. End of Session Thank You For your participation. Feedback Please provide feedback. Contact Reach out with questions.