DATA MODELING Training

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DATA MODELING Training


 (4.9) | 350 Ratings


Introduction


DATA MODELING Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Module 1 : Introduction to Data Modelling

Topics : Why Model? , Importance of logical data modeling, Database and Application Development Life , Cycle, Process Modeling, Logical Data Modeling, Database Design, Database Generation, Data Type Model

Module 2 :  Introduction to Logical Data Modeling

Topics : Importance of logical data modeling in requirements, Relationship between logical and physical data model a logical data model Elements, Read a high-level data model, Data model prerequisites, Data model sources of information, developing a logical data model

Module 3 : Project Context and Drivers

Topics :  Importance of well-defined solution scope, Functional decomposition diagram, Context-level data flow diagram, Sources of requirements, Functional decomposition and Data flow diagrams, Use case models, Workflow models, Business intelligence and data warehousing systems, Integration and consolidation of existing systems

Module 4 : Conceptual Data Modeling

Topics : Discovering entities, Defining entities, documenting an entity, Identifying attributes, distinguishing between entities and attributes

Module 5 : Conceptual Data Modeling-Identifying Relationships and Business Rules

Topics : Model fundamental relationships, Cardinality of relationships, One-to-one, One-to-many, Many-to-many, Naming the relationships

Module 6 : Identifying Attributes

Topics : Discover attributes for the subject area, Assign attributes to the appropriate entity, Name attributes using established naming conventions, Documenting attributes

Module 7 : Advanced Relationships

Topics : Modeling many-to-many relationships, Model multiple relationships between the same two entities, Model self-referencing relationships, Model ternary relationships, Identify redundant relationships

Module 8 : Completing the Logical Data Model

Topics : Use supertypes and subtypes to manage complexity, Use supertypes and subtypes to represent rules and constraints

Module 9 : Data Integrity through Normalization

Topics : Normalize a logical data model, First normal form, Second normal form, Third normal form, Reasons for denormalization, Transactional vs. business intelligence applications, Verification and Validation, Data flow diagram, CRUD matrix

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Course Curriculum

Module 1 : Introduction to Data Modelling

Topics : Why Model? , Importance of logical data modeling, Database and Application Development Life , Cycle, Process Modeling, Logical Data Modeling, Database Design, Database Generation, Data Type Model

Module 2 :  Introduction to Logical Data Modeling

Topics : Importance of logical data modeling in requirements, Relationship between logical and physical data model a logical data model Elements, Read a high-level data model, Data model prerequisites, Data model sources of information, developing a logical data model

Module 3 : Project Context and Drivers

Topics :  Importance of well-defined solution scope, Functional decomposition diagram, Context-level data flow diagram, Sources of requirements, Functional decomposition and Data flow diagrams, Use case models, Workflow models, Business intelligence and data warehousing systems, Integration and consolidation of existing systems

Module 4 : Conceptual Data Modeling

Topics : Discovering entities, Defining entities, documenting an entity, Identifying attributes, distinguishing between entities and attributes

Module 5 : Conceptual Data Modeling-Identifying Relationships and Business Rules

Topics : Model fundamental relationships, Cardinality of relationships, One-to-one, One-to-many, Many-to-many, Naming the relationships

Module 6 : Identifying Attributes

Topics : Discover attributes for the subject area, Assign attributes to the appropriate entity, Name attributes using established naming conventions, Documenting attributes

Module 7 : Advanced Relationships

Topics : Modeling many-to-many relationships, Model multiple relationships between the same two entities, Model self-referencing relationships, Model ternary relationships, Identify redundant relationships

Module 8 : Completing the Logical Data Model

Topics : Use supertypes and subtypes to manage complexity, Use supertypes and subtypes to represent rules and constraints

Module 9 : Data Integrity through Normalization

Topics : Normalize a logical data model, First normal form, Second normal form, Third normal form, Reasons for denormalization, Transactional vs. business intelligence applications, Verification and Validation, Data flow diagram, CRUD matrix

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