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Data Modeling
New Age Technologies has been delivering Authorized Training since 1996. We offer a full suite of Business Analysis courses including courses pertaining to Data Modeling, Software Testing, Enterprise Analysis, Data Modeling, Business Analysis Foundations, Software Development and more. If you have any questions or can’t seem to find a Business Analysis class that you are interested in, contact one of our Training Specialists. Invest in your future today with Business Analysis training from New Age Technologies.
✉ NewAT Training Specialists | ☏ 502.909.0819
COURSE INFORMATION
Learn to create use data models.
Data Modeling Course Overview:
Since a business analyst needs to accurately elicit, define, and document user requirements, understanding the users’ needs is key to an analyst’s success. By using logical data modeling, a business analyst can convey requirements in a way that can easily be validated, and doing so allows stakeholders to understand the requirements, business rules, and data management methods for any given project.
In this class, get hands-on practice modeling requirements through entity relationship diagrams, supertypes and subtypes, and attributive and associative entities. You will learn to use logical data modeling to work directly with business users to accurately define requirements.
Students pursuing a university-recognized and/or accredited certificate in Canada or continuing education units in the US must attend at least 90% of class time, participate in class exercises and section-knowledge checks, and score at least 70% on an end-of-class, multiple-choice assessment.
Who Should Attend:
- Systems analysts, business analysts, IT project managers, associate project managers, project managers, project coordinators, project analysts, project leaders, senior project managers, team leaders, product managers, and program managers
Data Modeling Course Prerequisites:
- Business Analysis Essentials
- Requirements Development, Documentation, and Management
- Use Case Modeling
Data Modeling Course Objectives:
After successfully completing this course, you will have learned the following:
- How logical data models relate to requirements
- Identifying entities and attributes
- Determining relationships and business rules
- Data integrity through normalization
Data Modeling Course Outline:
Module 1: Introduction to Logical Data Modeling
- Importance of logical data modeling in requirements
- When to use logical data models
- Relationship between logical and physical data model
- Elements of a logical data model
- Read a high-level data model
- Data model prerequisites
- Data model sources of information
- Developing a logical data model
Module 2: Project Context and Drivers
- Importance of well-defined solution scope
- Functional decomposition diagram
- Context-level data flow diagram
- Sources of requirements
- Functional decomposition diagrams
- Data flow diagrams
- Use case models
- Workflow models
- Business rules
- State diagrams
- Class diagrams
- Other documentation
- Types of modeling projects
- Transactional business systems
- Business intelligence and data warehousing systems
- Integration and consolidation of existing systems
- Maintenance of existing systems
- Enterprise analysis
- Commercial off-the-shelf application
Module 3: Conceptual Data Modeling
- Discovering entities
- Defining entities
- Documenting an entity
- Identifying attributes
- Distinguishing between entities and attributes
Module 4: Conceptual Data Modeling-Identifying Relationships and Business Rules
- Model fundamental relationships
- Cardinality of relationships
- One-to-one
- One-to-many
- Many-to-many
- Is the relationship mandatory or optional?
- Naming the relationships
Module 5: Identifying Attributes
- Discover attributes for the subject area
- Assign attributes to the appropriate entity
- Name attributes using established naming conventions
- Documenting attributes
Module 6: Advanced Relationships
- Modeling many-to-many relationships
- Model multiple relationships between the same two entities
- Model self-referencing relationships
- Model ternary relationships
- Identify redundant relationships
Module 7: Completing the Logical Data Model
- Use supertypes and subtypes to manage complexity
- Use supertypes and subtypes to represent rules and constraints
Module 8: Data Integrity Through Normalization
- Normalize a logical data model
- First normal form
- Second normal form
- Third normal form
- Reasons for denormalization
- Transactional vs. business intelligence applications
Module 9: Verification and Validation
- Verify the technical accuracy of a logical data model
- Use CASE tools to assist in verification
- Verify the logical data model using other models
- Data flow diagram
- CRUD matrix
