Data Modeling

(DATA-MODEL.AO1) / ISBN : 978-1-64459-301-1
This course includes
Lessons
TestPrep
Hands-On Labs
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About This Course

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.

Skills You’ll Get

Get the support you need. Enroll in our Instructor-Led Course.

Lessons

13+ Lessons | 101+ Exercises | 178+ Quizzes | 107+ Flashcards | 107+ Glossary of terms

TestPrep

52+ Pre Assessment Questions | 52+ Post Assessment Questions |

Hands-On Labs

21+ LiveLab | 18+ Video tutorials | 00+ Minutes

1

Introduction

  • Who Should Read This Course
  • What the Course Covers
2

Introduction to Data Modeling

  • 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
3

Relational Model Components

  • Conceptual and Logical Model Components
  • Physical Model Components
4

Data and Process Modeling

  • Data Model Diagramming Alternatives
  • Process Models
  • Unified Modeling Language (UML)
  • Relating Entities and Processes
5

Organizing Database Project Work

  • The Traditional Life Cycle
  • Nontraditional Life Cycles
  • The Project Triangle
6

Conceptual Data Modeling

  • The Conceptual Modeling Process
  • Creating the Model
  • Evaluating the Model
7

Logical Database Design Using Normalization

  • The Need for Normalization
  • Applying the Normalization Process
  • Denormalization
  • Practice Problems
8

Beyond Third Normal Form

  • Advanced Normalization
  • Resolving Supertypes and Subtypes
  • Generalizing Attributes
  • Alternatives for Reference Data
9

Physical Database Design

  • The Physical Design Process
  • Designing Tables
  • Integrating Business Rules and Data Integrity
  • Adding Indexes for Performance
  • Designing Views
10

Alternatives for Incorporating Business Rules

  • 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
11

Alternatives for Handling Temporal Data

  • Temporal Data Structures
  • Calendar Data Structures
  • Business Rules for Temporal Data
12

Modeling for Analytical Databases

  • Data Warehouses
  • Data Marts
  • Modeling Analytical Data Structures
  • Loading Data into Analytical Databases
13

Enterprise Data Modeling

  • Enterprise Data Management
  • The Enterprise Data Model

1

Introduction to Data Modeling

  • Creating a Conceptual Model
  • Creating a Physical Data Model
  • Creating a Logical Data Model
2

Relational Model Components

  • Modifying a Conceptual Model
3

Data and Process Modeling

  • Drawing of a Conceptual Model with Nested Subtypes
4

Organizing Database Project Work

  • Discussing the Traditional Life Cycle and Requirements Gathering
  • Testing the Knowledge of Project Database Management Tasks
  • Discussing Nontraditional Life Cycles and the Project Triangle
5

Conceptual Data Modeling

  • Creating a Conceptual Model for the Employee Management System
6

Logical Database Design Using Normalization

  • Creating a Data Model in Second Normal Form
  • Creating a Data Model in First Normal Form
  • Analyzing Normalization in Academic Tracking Database
7

Beyond Third Normal Form

  • Creating a Data Model in Fourth Normal Form
  • Creating a Complex Logical Data Model
8

Physical Database Design

  • Converting a Logical Data Model into a Physical Data Model
  • Creating a Physical Data Model ERD
  • Creating a Data Model in Third Normal Form
9

Alternatives for Incorporating Business Rules

  • Modeling Business Rules in a Logical Data Model
10

Alternatives for Handling Temporal Data

  • Adding History to Data Models
11

Modeling for Analytical Databases

  • Designing a Star Schema Fact Table
12

Enterprise Data Modeling

  • Developing an Enterprise Conceptual Model

Data Modeling

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