Data Modeling

The Data Modeling course and lab cover the entire field of how to create data models that allow complex data to be analyzed, manipulated, extracted, and reported upon accurately. The labs are cloud-based, device-enabled, and can easily be integrated with an LMS. The computer architecture course and lab also provide knowledge on the areas such as I/O functions and structures, RISC, and parallel processors with real-world examples enhancing the text for reader interest.
Here's what you will get

Lessons
  • 13+ Lessons
  • 101+ Exercises
  • 178+ Quizzes
  • 107+ Flashcards
  • 107+ Glossary of terms
TestPrep
  • 52+ Pre Assessment Questions
  • 52+ Post Assessment Questions
LiveLab
  • 21+ LiveLab
  • 18+ Video tutorials
Here's what you will learn
Download Course Outline
  • Who Should Read This Course
  • What the Course Covers
  • Data-Centric Design
  • Anatomy of a Data Model
  • Importance of Data Modeling
  • Measures of a Good Data Model
  • How Data Models Fit Into Application Development
  • Data Modeling Participants
  • Conceptual and Logical Model Components
  • Physical Model Components
  • Data Model Diagramming Alternatives
  • Process Models
  • Unified Modeling Language (UML)
  • Relating Entities and Processes
  • The Traditional Life Cycle
  • Nontraditional Life Cycles
  • The Project Triangle
  • The Conceptual Modeling Process
  • Creating the Model
  • Evaluating the Model
  • The Need for Normalization
  • Applying the Normalization Process
  • Denormalization
  • Practice Problems
  • Advanced Normalization
  • Resolving Supertypes and Subtypes
  • Generalizing Attributes
  • Alternatives for Reference Data
  • The Physical Design Process
  • Designing Tables
  • Integrating Business Rules and Data Integrity
  • Adding Indexes for Performance
  • Designing Views
  • The Anatomy of a Business Rule
  • Implementing Business Rules in Data Models
  • Limitations on Implementing Business Rules in Data Models
  • Functional Classification of Business Rules
  • Temporal Data Structures
  • Calendar Data Structures
  • Business Rules for Temporal Data
  • Data Warehouses
  • Data Marts
  • Modeling Analytical Data Structures
  • Loading Data into Analytical Databases
  • Enterprise Data Management
  • The Enterprise Data Model

Hands on Activities (Live Labs)

  • Creating a Conceptual model
  • Creating a Physical Data Model
  • Creating a Logical Data Model
  • Modifying a Conceptual Model
  • Drawing of a Conceptual Model with Nested Subtypes
  • Discussing the Traditional Life Cycle and Requirements Gathering
  • Testing the Knowledge of Project Database Management Tasks
  • Discussing Nontraditional Life Cycles and the Project Triangle
  • Creating a Conceptual Model for the Employee Management System
  • Creating a Data Model in Second Normal Form
  • Creating a Data Model in First Normal Form
  • Analyzing Normalization in Academic Tracking Database
  • Creating a Data Model in Fourth Normal Form
  • Creating a Complex Logical Data Model
  • Converting a Logical Data Model into a Physical Data Model
  • Creating a Physical Data Model ERD
  • Creating a Data Model in Third Normal Form
  • Modeling Business Rules in a Logical Data Model
  • Adding History to Data Models
  • Designing a Star Schema Fact Table
  • Developing an Enterprise Conceptual Model
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