DAMA DMBoK Data Modelling Specialist Training

This 1-day DAMA DMBoK Data Management Specialist course addresses all disciplines as identified Data Modeling specialist exam by the international standard, DAMA Body of Knowledge (DMBOK2) and is aimed to individuals interested in developing concrete professionalism in the field of Data Management with a specialisation in Data Modelling And Design

This course provides a solid foundation of the different information disciplines across the complete Data Modeling, Data Management spectrum and introduces students to the different levels of Industry professional certification the DAMA Certified Data Modelling And Design Professional (CDMP).

The data modeling specialist course explores different data modeling methodologies. It helps understand how to select, implement, and interpret suitable statistical analyses and designs for practical data scenarios and problems. In addition, it explores various features of data modeling including Data Models, Data Implementation, Design Quality Management, Data Designs, Modeling Styles, and System Development Lifecycle among others.

DAMA DMBoK Data Management Fundamentals Recommended prerequisites:

• CDMP-DMF - DAMA DMBoK Data Management Fundamentals

• To help understand data modeling methodologies.
• To help comprehend how statistical designs and analyses work.
• To help teach the application of data models.
• To help comprehend different data model styles.
• To explain how different data designs and models may be utilized for business problems and scenarios.
• To explain how System Development Lifecycle works.

• Database Design and Data Modeling
• CDMP Breakdown
• Explanation of Data Design and Modeling
• Comprehension of SDLC and Lifecycle
• Understanding the Development of Data
• Explanation of a Data Model
• Representations of Data Models
• Significance of Data Modeling
• Reasons for the Production of a Data Model
• Data Model Levels
• Symbols of Entity Relationships
• Notations and Cardinality
• Enterprise Data Model
• Physical Data Model
• Logical Data Models
• Conceptual Data Models
• Subject Area Models
• Independent and Dependent Entities
• Significance of Comprehending Entities
• Types of Relationships
• Recursive Relationships
• Many to Many Relationships
• Entity Subtypes
• Role Name Usage
• Physical and Conceptual Terms
• Primary Keys
• Entity Keys
• Alternate Keys
• Benefits and Drawbacks of Natural versus Surrogate Keys
• Data Model and Process Relationships
• Components of a Logical Data Model
• Normalization Rules
• Normalization Approaches
• Normalization Definition
• Principles of Database Design
• Best Practices of a Physical Database Design
• Comprehension of Partitioning
• Transformation of Logical to Physical Data Model
• ACID Test
• Classification of Modeling Tools
• The Alternative to ACID