Data Modeling Training
Anyone can start Data Modeling Training course because there are no prior requirements necessary. We provide a 18-hour Data Modeling course taught by instructors with more than 10 years of real-time experience. The course includes real-world assignments and the faculty will direct you toward setting a work environment to practice assignments.
Assistance in CV preparation, interview questions answers, and materials are part of the training program. We do advise taking a one-hour session every day, From Monday through Friday, but one can also look into the weekend, fast-track, one-on-one, or customized programs.
Data Modeler Career
✔️Data Modeling Pre-requisites
Basic Computer Knowledge.
✔️Jobs On Data Modeling
Top IT MNC such as Capgemini, Cognizant, IBM, Infosys, Accenture, etc.,
✔️Data Modeler Salary
The average salary for a Data Modeler with 4 years experience in India is ₹25.3 Lakhs.
Data Modeling Course FAQ's
01. can you help me in CV preparation?
Yes, We can help you preparing your resume.
02. will you help in interview preparation?
We can provide you interview question answers. The course covered many real-time examples. These examples might help you.
03. Is this course sufficient to get a job?
Our training covered as many real-time examples as we can. This course may equivalent to 2 to 3 years of work experience. You have to work hard if you are aimed at 4+ years of experience.
04. What’s the certification process?
Please come with an exam code. We’ll guide you further. We’ll guide you on how to get certified. Don’t worry, we’ll help you in certification process.
05. Can you provide Work Support?
We can provide job support for an additional fee. Contact the support team for fee details. You can choose either the hourly rate or monthly fee.
01. 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
2. Project Context and Drivers
- Importance of well-defined solution scope
- Functional decomposition diagram
- Context-level data flow diagram
- Sources of requirements
- Types of modeling projects
03. Conceptual Data Modeling
- Discovering entities
- Defining entities
- Documenting an entity
- Identifying attributes
- Distinguishing between entities and attributes
04. Conceptual Data Modeling-Identifying Relationships and Business Rules
- Model fundamental relationships
- Cardinality of relationships
- Is the relationship mandatory or optional?
- Naming the relationships
05. Identifying Attributes
- Discover attributes for the subject area
- Assign attributes to the appropriate entity
- Name attributes using established naming conventions
- Documenting attributes
06. 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
07. Completing the Logical Data Model
- Use supertypes and subtypes to manage complexity
- Use supertypes and subtypes to represent rules and constraints
08. Data Integrity Through Normalization
- Normalize a logical data model
- Reasons for denormalization
- Transactional vs. business intelligence applications
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