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Building the Logical Data Model - Online

Course Code: STTA DATAM
Length: 3 Days
Tuition: $1,395.00
Official

Schedule for this Course

There are no dates scheduled for this course.
If you would like to be added to the wait list for this class Click Here

Course Description:

PMI: 14 PDUs
IIBA: 14 CDUs

Data Analysis Training - Learn to Apply Proven Techniques for Data Modeling and Improve Business Processes.

If you work with distributed teams, including offshore developers and testers, you know that the more distant the development team, the greater the need for precision. Precise doesn't mean bigger documents in more abstruse notations. In this course you will learn a simple and compact system for collaborative modeling that enables you to capture the most information in the smallest space with the least work in a way that's easily testable and highly adaptable. By doing this precise data analysis you will deliver more value in less time with higher quality.

Objectives:

  • Learn how to organize a problem domain's concepts into a formal and accurate relational data model.
  • Create tables, columns, relations, and constraints that accurately reflect data requirements.
  • Use basic principles of normalization to ensure consistency. Understand when "de-normalization" is and is not appropriate.
  • Create non-relational data models (such as XML schemas) and learn how to convert between relational and nonrelational models.

Who Should Attend

This course is valuable for anyone who needs to accurately understand and manage the role of data and information in any given business processes or area. Perfect for:

  • Business Analysts
  • DBAs
  • Data Modelers
  • Data Analysts
  • Process Modelers
  • Project Managers

Prerequisites

n/a

Course Outline:

Section 1. Basics of the Relational Data Model?

  •     Logical vs. Physical Models
  •     Tables, Columns, Keys, and Relations
  •     Entity-Relationship Diagramming (ERDs) and UML Class Diagrams

Section 2. Normalization Techniques

  •     Why is normalization important?
  •     Common Normal Forms
  •     Real-World Data Issues

Section 3. Data Models in Context

  •     Evaluating Data Requirements
  •     Data Analysis in Business Process Models
  •     Dealing with Legacy Systems and Data

Section 4. Applying Data Models

  •     Data Models and UI Designs
  •     Hierarchical Data Models (e.g. XML)
  •     Object-Relational Mapping

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