Data Modeling Course Overview

Target Audience for Data Modeling

Koenig Solutions' Data Modeling course provides in-depth training on creating effective logical data models for IT professionals.

• Data Analysts
• Business Analysts
• Database Administrators (DBAs)
• Data Architects
• Data Scientists
• Software Engineers
• Database Designers
• Information Modelers
• IT Project Managers
• Systems Analysts
• BI Professionals
• Enterprise Architects
• Database Developers
• Data Warehouse Specialists
• Quality Assurance Engineers
• Technical Product Managers

Learning Objectives - What you will Learn in this Data Modeling?

Introduction to the Course's Learning Outcomes:

This Data Modeling course equips students with the skills to create logical data models, understand their relationship to physical models, and apply best practices in real-world scenarios.

Learning Objectives and Outcomes:

• Grasp the critical role of logical data modeling in capturing and defining system requirements.
• Recognize appropriate scenarios for employing logical data models to ensure clarity and efficiency.
• Differentiate between logical and physical data models and understand their interconnection.
• Identify and utilize the core elements that constitute a logical data model, including entities, attributes, and relationships.
• Acquire the ability to read and interpret high-level data models to facilitate better communication among stakeholders.
• Learn the prerequisites for effective data modeling, including gathering and analyzing relevant sources of information.
• Develop a logical data model from scratch by incorporating entity discovery, attribute identification, and relationship mapping.
• Address project scope and drivers through functional decomposition and data flow diagrams to set clear boundaries for the modeling effort.
• Master the conceptualization of data modeling by discovering and defining entities, documenting their nature, and distinguishing them from attributes.
• Determine and model different types of relationships, their cardinality, optionality, and enforce business rules through naming conventions.
• Refine the logical data model by applying concepts of supertypes and subtypes to represent complex rules and manage data structure intricacies.
• Enhance data integrity and optimize model performance through normalization techniques, and understand when denormalization is beneficial.
• Verify and validate the logical data model's accuracy using technical review processes and CASE tools, ensuring alignment with other system models.