Data Modelling and Design Course Overview

Data Modelling and Design Course Overview

The Data Modelling and Design course provides a comprehensive foundation for understanding and creating effective data models, which are crucial for developing robust databases and information systems. In Module 1, learners are introduced to the concept of modeling, exploring the reasons for modeling, project phases, model transitions, and function model terminology. This sets the stage for practical applications in data design.

Module 2 dives into creating the Entity-Relationship Model, a cornerstone of data modeling that helps define the structure of data and its interrelationships. Through lessons on identifying entities and relationships, as well as understanding relationship types and unique identifiers, students will learn how to create detailed and accurate ER models.

In Module 3, the course explores Normalization, a process vital for reducing redundancy and improving data integrity within databases. Learners will gain insights into relational database principles and the transition from 0NF to 1NF, equipping them with the skills necessary to structure data efficiently.

Finally, Module 4 guides learners through the stages of Design and Implement Data Models, covering the transitions from analysis to design, from entities to tables, and the implementation of subtypes and supertypes. This includes lessons on denormalization and the transition of entity models to Unified Modeling Language (UML) class models.

Overall, this course will help learners master the art of creating structured, efficient, and maintainable data models, an essential skill in the field of data management and database design.

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  • Live Online Training (Duration : 16 Hours)
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♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

Certainly! Here are the minimum required prerequisites for successfully undertaking the Data Modelling and Design course:

  • Basic understanding of databases and their purpose in managing data.
  • Familiarity with the foundational concepts of database systems (e.g., tables, records, fields).
  • An introductory level of knowledge in SQL or any other database query language.
  • General awareness of the software development lifecycle and the role that data modeling plays within it.
  • Ability to understand and use basic computer software, such as word processors and spreadsheets.
  • Logical thinking and problem-solving skills to conceptualize how data is related.
  • Willingness to learn new concepts and apply them to practical scenarios.

These prerequisites are designed to ensure that all participants have a foundational base upon which to build their understanding of data modeling and design. No advanced technical skills are required to begin this course, as it is structured to guide learners from fundamental principles to more complex concepts.

Target Audience for Data Modelling and Design

The Data Modelling and Design course equips participants with essential skills for efficient database structure creation and management.

  • Database Designers
  • Data Architects
  • Data Modellers
  • Business Analysts
  • System Analysts
  • IT Project Managers
  • Database Administrators (DBAs)
  • Software Engineers
  • Application Developers
  • Data Analysts
  • Data Scientists (with a focus on data structure)
  • BI (Business Intelligence) Professionals
  • Data Engineers

Learning Objectives - What you will Learn in this Data Modelling and Design?

Introduction to the Course’s Learning Outcomes and Concepts Covered:

This course offers a comprehensive understanding of data modeling and design, covering the creation of entity-relationship models, normalization, and the transition from model to implementation.

Learning Objectives and Outcomes:

  • Grasp the fundamental concepts and importance of data modeling in the context of IT systems development.
  • Understand the role of data modeling within different project phases and how models evolve through these phases.
  • Learn how to create and refine Entity-Relationship (ER) models, including identifying entities and relationships.
  • Differentiate between various relationship types and master the identification and validation of unique entity identifiers.
  • Gain the ability to refactor attributes into entities and understand the use of supertype-subtype entities in data models.
  • Recognize and apply common data model patterns such as Master-Detail, Classification, and Basket Patterns.
  • Comprehend the principles of normalization and learn how to transition from 0NF to 1NF to create well-structured databases.
  • Develop skills in transitioning from analysis to design and then to implementation, with a focus on entity to table transitions.
  • Learn the strategies for denormalization and when its application is appropriate for performance and practicality.
  • Acquire the capability to translate entity models into UML class models for object-oriented design and implementation.