Data Modelling Techniques In Data Analytics.pptx

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1. Data Modelling Techniques In Data Analytics DATA ANALYTICS THEORY PPT

2. Introduction to Data Modelling Techniques Data modelling techniques are essential for organizing and representing data effectively in analytics projects. They help in understanding the structure, relationships, and constraints within data sets. Choosing the right technique depends on the specific goals and complexity of the data analysis task.

3. Conceptual Data Modelling Conceptual models provide a high-level overview of data without technical details. They typically use diagrams like Entity-Relationship (ER) diagrams to depict data entities and their relationships. This approach facilitates communication between stakeholders and data architects by focusing on business concepts.

4. Logical Data Modelling Logical models translate conceptual designs into detailed, platform-independent structures. They specify data types, keys, and relationships, ensuring data integrity and consistency. Logical modelling supports normalization to reduce redundancy and improve data quality.

5. Physical Data Modelling Physical models focus on how data is stored and accessed in specific database systems. They include details like table structures, indexes, partitioning, and storage considerations. This technique optimizes performance and efficiency for the targeted database environment.

6. Conclusion and Best Practices Selecting the appropriate data modelling technique depends on the project scope, complexity, and end-user needs. Combining multiple techniques can provide a comprehensive framework for complex analytics environments. Continuous validation and refinement of data models ensure they remain aligned with evolving business requirements.