Mastering Data Modeling Fundamentals

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  • Cost
    Paid
  • Provider
    Udemy
  • Certificate
    No Information
  • Language
    English
  • Start Date
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  • Learners
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4.4
33,500 Ratings
This course is perfect for anyone looking to master the fundamentals of data modeling. Learn the techniques required to create data models for your organisation, including both the classic entity-relationship notation and the "crow's foot" notation. You will also learn how to create semantically correct data models from entities, attributes, relationships, hierarchies, and other modelling components. Finally, you will be able to use forward engineering to convert conceptual data models to logical and physical data models. Sign up now to become a data modeling expert!
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Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 18th, 2023]

This course, Mastering Data Modeling Fundamentals, provides learners with the techniques required to create data models for their organisation. Learners will use both the classic entity-relationship notation and the "crow's foot" notation to apply essential data modelling design ideas. They will also learn how to create semantically correct data models from entities, attributes, relationships, hierarchies, and other modelling components. Finally, learners will be introduced to forward engineering, which is used to convert conceptual data models to logical and physical data models.

Course Syllabus

Introduction to Data Modeling Fundamentals

The Purpose and Role of Data Modeling

Basic Data Modeling Concepts and Terminology

Navigate Your Alternatives Among Methodologies, Techniques, and Notations

Compare Data Modeling to Database Design

Compare Transactional Data Modeling to Analytical Data Modeling

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Pros & Cons
  • Clear and concise explanations
  • Practical examples and application
  • Lack of resources
  • Insufficient explanations on normalization
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