Data Modeling: Develop Depth to Your Models

Unfiled

Serebra Learning Corporation

This is the second course in a two-part series which covers Data Modeling. You will begin by learning a top-down systematic approach to database development using entity-relationship models and normalization. Then you will use this approach to identify and define business information requirements both in the course and within your work environment. This course can be used as part of Oracle's integrated curriculum. It is designed to act as an alternative prerequisite to a large number of ILT courses in related curricula areas such as Server Developer/2000 and Designer/2000.

This is primarily online training
on-line e-learning cbt (computer based)This is an online eLearning or CBT training program
Duration:8 hours
Training Presented in:English
Training Provided by Serebra Learning Corporation
  • L asked: What is the name of the first course?
Data Modeling: Develop Depth to Your Models
Audience

Business managers business analysts technical analysts database designers database administrators and anyone responsible for the analysis and specification of data during the application development lifecycle. Participants should have knowledge equivalent to: course 60021 Data Modeling: Create Basic Models.

Objective

  • Model hierarchies and roles.
  • Model exclusive entities and relationships.
  • Model data which reflects changes over time.
  • Resolve fan traps and chasm traps.
  • Consider effects of convergent and divergent models.
  • Model nontransferable relationships.
  • Define normalization.
  • Explain the benefits of Normalization.
  • Place an Entity Relationship (ER) Model fully in the Third Normal Form (TNF).
  • Consider the value of Normalization and ER Modeling. Use normalization as a QA technique for entities.

Topics Include

Unit 1: Adding Complexity to an Entity Relationship (ER) Model

  • Identify what a hierarchy is.
  • Create a business hierarchy using One : Many relationships.
  • Create a business hierarchy using a recursive relationship.
  • Resolve a M:M recursive relationship to create a BOM structure.
  • Create an ER diagram to reflect the roles in a business scenario.
  • Identify what exclusivity is.
  • Identify what exclusive entities are.
  • Create an ER model for a business scenario using supertype/subtype construct.
  • Identify the business scenario being modeled in a supertype/subtype construct.
  • Identify what exclusive relationships are.
  • Create an ER model for a business scenario using an arc construct.
  • Identify the business scenario being modeled in an arc construct.
  • Identify when an arc should not be used as an alternative to a supertype/subtype.
  • Identify the need to hold historical data.
  • Create an ER model to represent historical data using a business scenario.
  • Identify the characteristics of a Data Warehouse.
  • Identify a Fan trap within an ER Model.
  • Identify a resolution for a Fan trap.
  • Identify a chasm trap within an ER model.
  • Identify a resolution for a chasm trap.
  • Identify the problems caused by too much convergence in an ER model.
  • Identify the problems caused by too much divergence in an ER model.
  • Identify why transferable relationships are needed.
  • Identify what a transferable relationship is.
  • Identify the symbol used to show non-transferability.
  • Identify the consequences of creating a non-transferable relationship.

Unit 2: The Technique of Normalization

  • Identify the reasons for performing normalization.
  • Identify the strengths of each technique.
  • Identify the meaning of Normalization terminology.
  • Identify the rules of normalization.
  • Identify the rule for moving creating unnormalized data.
  • Create a UNF data group.
  • Identify the rule for moving data from unnormalized into first normal form (FNF).
  • Identify the additional steps necessary to place all the data groups in first normal form.
  • Create a FNFdata group.
  • Identify the rule for moving data from first into second normal form (SNF).
  • Identify the additional steps necessary to place all the groups in second normal form.
  • Create a SNF data group.
  • Identify the rule for moving data from second into third normal form.
  • Identify the additional steps necessary to place all the groups in third normal form.
  • Create a TNF data group.
  • Identify the rule for moving data from third into Boyce Codd normal form (BCNF).
  • Identify the additional steps necessary to place all the groups in BC normal form.
  • Create a BCNF data group.
  • Identify the tests needed to ensure that the groups are fully in third normal form.
  • Identify why it is necessary to optimize data groups.
  • Identify the problems with optimized data groups.
  • Create optimized data groups from given groups.
  • Identify the reasons for retesting after optimization.
  • Create a diagram from a set of normalized data groups.

Unit 3: Combining Normalization and ER Modeling

  • Identify the strengths and weaknesses of both techniques.
  • Identify the benefits of normalizing an ER model.
  • Identify entities which are not fully in Third Normal form.

Duration

8

Minimum Requirements

The CDROM version of this course requires:

  • At least a 486DX 33Mhz CPU.
  • Microsoft Windows 3.1 or higher and a Microsoft compatible mouse.
  • At least 8MB RAM.
  • At least VGA graphics capability with a minimum 512K video RAM (1MB video RAM recommended).
  • At least a double speed CDROM drive.
  • An MPC compliant sound card with attached speakers or headphones is recommended (Currently only the CDROM version supports audio).
The network version of this course requires:
  • At least a 486DX 33Mhz CPU.
  • Microsoft Windows 3.1 or higher and a Microsoft compatible mouse.
  • At least 8MB RAM and 22MB available hard disk space or file server space.
  • At least VGA graphics capability with a minimum 512K video RAM (1MB video RAM recommended).

Media

Serebra Learning Corporation 119 - 7565 132nd Street Surrey BC    V3W 1K5 Canada
About The Training Provider: Serebra Learning Corporation
Serebra Learning Corporation - Serebra Learning Corporation provides technology-based training solutions through a combination of Cortex, its proprietary learning management system (LMS), and a curriculum catalog with over 1, 825 current courseware titles. Founded in 1987 (as FirstClass Systems, with a name change to Serebra in 2001), Serebra has over sixteen years" experience delivering e-learning solutions to both...
Want to market your data modeling training?
Custom Search
tcw11-v473M-09/01/11-13:02:42-()[B]-[A]-[A] -06:30:25