Course Summary

This course is designed to equip students with the essential knowledge and skills required to become proficient Data Modelers. Participants will learn how to analyze data requirements, design and implement data models, and optimize them for performance and scalability.

By the end of the course, learners will have a comprehensive understanding of various data modeling techniques, methodologies, and tools used in the industry to structure data for effective storage, retrieval, and analysis.

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Introduction to Data Modeling:
- Concepts and importance of data modeling
- Types of data models (Conceptual, Logical, Physical)
- The role of a data modeler
Entity-Relationship (ER) Modeling:
- ER diagrams: Entities, Attributes, and Relationships
- Identifying Primary and Foreign Keys
- Normalization (1NF, 2NF, 3NF, BCNF)
Dimensional Modeling:
- Star Schema and Snowflake Schema
- Fact Tables and Dimension Tables
- Measures, hierarchies, and aggregations
Database Design Principles:
- SQL database modeling vs. NoSQL database modeling
- Data integrity, constraints, and relationships
- Data redundancy and optimization techniques
Advanced Data Modeling Techniques:
- Data Warehousing and Data Lakes
- Designing for Big Data and real-time systems
- Using tools like ERwin, PowerDesigner, and SQL Developer for data modeling
Data Model Optimization and Performance:
- Indexing strategies
- Partitioning and denormalization techniques
- Performance tuning and best practices for scalability
Real-World Data Modeling Applications:
- Industry case studies
- Data governance and regulatory compliance (GDPR, HIPAA, etc.)
- Working with diverse datasets (structured, unstructured, semi-structured)

Quizzes & Tests: Periodic assessments on key concepts, ensuring students understand fundamental principles of data modeling.
Assignments: Practical exercises on creating ER models, normalizing databases, and constructing dimensional models.
Capstone Project: Design and implement a comprehensive data model for a real-world case study, incorporating both relational and dimensional models.
Peer Review: Collaborative group work and peer feedback on data models.
Presentation: A final presentation showcasing the students’ data model solution for their capstone project.

By the end of the course, students will be able to:

Understand and apply the fundamental concepts of data modeling.
Design effective ER and dimensional models that support business needs.
Implement and optimize data models in relational and NoSQL databases.
Analyze and solve real-world data modeling challenges, ensuring efficiency and performance.
Use data modeling tools to create and manage complex database architectures.
Implement data integrity, consistency, and security in database designs.
Apply industry best practices for data model optimization and scalability.
Communicate data modeling solutions to both technical and non-technical stakeholders.

Regular & Intensive Learning Option

Class Schedule:
Morning Students – Monday to Thursday, 09:00 am – 03:00 pm
Afternoon Students – Monday to Thursday, 03:30 pm – 08:00 pm
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 2 sessions (6 months)
Session Length: 1 session = 3 months
DWTA Qualification: Academic Certificate
CTVET’s National TVET Qualification Framework (NTVETQF): National Certificate I
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Slow-Paced & Relaxed Learning Option

Class Schedule:
Weekend Evening Students – Friday to Sunday, 06:00 pm – 08:00 pm
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 2 sessions (6 months)
Session Length: 1 session = 3 months
DWTA Qualification: Academic Certificate
CTVET’s National TVET Qualification Framework (NTVETQF): National Certificate I
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Fill Application Form

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Data Modeler

Course Summary

Introduction to Data Modeling:

Concepts and importance of data modeling

Types of data models (Conceptual, Logical, Physical)

The role of a data modeler

Entity-Relationship (ER) Modeling:

ER diagrams: Entities, Attributes, and Relationships

Identifying Primary and Foreign Keys

Normalization (1NF, 2NF, 3NF, BCNF)

Dimensional Modeling:

Star Schema and Snowflake Schema

Fact Tables and Dimension Tables

Measures, hierarchies, and aggregations

Database Design Principles:

SQL database modeling vs. NoSQL database modeling

Data integrity, constraints, and relationships

Data redundancy and optimization techniques

Advanced Data Modeling Techniques:

Data Warehousing and Data Lakes

Designing for Big Data and real-time systems

Using tools like ERwin, PowerDesigner, and SQL Developer for data modeling

Data Model Optimization and Performance:

Indexing strategies

Partitioning and denormalization techniques

Performance tuning and best practices for scalability

Real-World Data Modeling Applications:

Industry case studies

Data governance and regulatory compliance (GDPR, HIPAA, etc.)

Working with diverse datasets (structured, unstructured, semi-structured)

Quizzes & Tests: Periodic assessments on key concepts, ensuring students understand fundamental principles of data modeling.

Assignments: Practical exercises on creating ER models, normalizing databases, and constructing dimensional models.

Capstone Project: Design and implement a comprehensive data model for a real-world case study, incorporating both relational and dimensional models.

Peer Review: Collaborative group work and peer feedback on data models.

Presentation: A final presentation showcasing the students’ data model solution for their capstone project.

By the end of the course, students will be able to:

Understand and apply the fundamental concepts of data modeling.

Design effective ER and dimensional models that support business needs.

Implement and optimize data models in relational and NoSQL databases.

Analyze and solve real-world data modeling challenges, ensuring efficiency and performance.

Use data modeling tools to create and manage complex database architectures.

Implement data integrity, consistency, and security in database designs.

Apply industry best practices for data model optimization and scalability.

Communicate data modeling solutions to both technical and non-technical stakeholders.

Regular & Intensive Learning Option

Class Schedule: Morning Students – Monday to Thursday, 09:00 am – 03:00 pm Afternoon Students – Monday to Thursday, 03:30 pm – 08:00 pm

Teaching Method: Online & Offline / Online only (eg. Google meet video call)

Course Duration: 2 sessions (6 months)

Session Length: 1 session = 3 months

DWTA Qualification: Academic Certificate

CTVET’s National TVET Qualification Framework (NTVETQF): National Certificate I

Tuition & Guidance Fees:

Ghc 2600 per session or

Ghc 900 per month

Slow-Paced & Relaxed Learning Option

Class Schedule: Weekend Evening Students – Friday to Sunday, 06:00 pm – 08:00 pm