Data Modeling
Develop the skills to model operational and analytical data for a business domain through a learning path centred on Data Modeling. Explore concepts, guided practice and an applied project.
Build practical Data Modeling skills.
Skills and knowledge developed in this course.
- Conceptual modelling
- Logical design
- Analytical design
- Physical validation
- Applied project: Model operational and analytical data for a business domain
- Data Modeling — validation and review
About this course
This course develops practical capability in Data Modeling. Its course-specific content covers Conceptual modelling, Logical design, Analytical design, Physical validation, followed by an applied project focused on model operational and analytical data for a business domain. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Data Modeling or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of data engineering & analytics. Prior experience with the named product helps with the practical exercises.
Model operational and analytical data for a business domain
Deliver model operational and analytical data for a business domain. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Conceptual modelling
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- Entities, relationships and business vocabulary
- Cardinality and optionality
Practical exerciseApply conceptual modelling to the model operational and analytical data for a business domain and capture the validation evidence.
02Logical design
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- Keys, dependencies and normalisation
- Constraints and temporal data requirements
Practical exerciseApply logical design to the model operational and analytical data for a business domain and capture the validation evidence.
03Analytical design
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- Grain, facts and dimensions
- Historical changes and semantic consistency
Practical exerciseApply analytical design to the model operational and analytical data for a business domain and capture the validation evidence.
04Physical validation
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- Indexes and storage considerations
- Sample queries, migrations and model documentation
Practical exerciseApply physical validation to the model operational and analytical data for a business domain and capture the validation evidence.
05Applied project: Model operational and analytical data for a business domain
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- Define inputs, assumptions and acceptance criteria for: Model operational and analytical data for a business domain.
- Implement the project using conceptual modelling, logical design, analytical design, physical validation.
Practical exerciseModel operational and analytical data for a business domain.
06Data Modeling — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Conceptual modelling · Logical design
Establish the Data Modeling scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Analytical design · Physical validation
Apply the core Data Modeling techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for model operational and analytical data for a business domain. - 04Learning milestone 4 In progress
Deliver the applied project
Complete model operational and analytical data for a business domain, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗