Online Data Modeling Training Syllabus

Online Training on Data Modeling end to end process with ERWIN Tool

 

Course Description:

This online training course explains in detail about database, data warehouse, data modeling concepts, data modeling types and how these are used in OLTP environments and Data warehouse / Datamart Environments. To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9884675745.

Course Information:
  • Start Date: 16th April, 2018
  • Course Name: Data Modeling Training
  • Course Fee: $130 (130 US Dollars)
  • Training Hours: 10.00 PM Eastern Standard Time to 11.00 PM Eastern Standard Time
  • Training Time: 1 hour per session
  • Days: Monday, Wednesday, Friday (Alternate Days)
  • Total no. of theoretical/Practical classes: At least 10
  • Mode of Teaching: Online through SKYPE
  • Instructor: Neelesh & Antony (Owner of LearnDataModeling.com)
  • Office: USA and Chennai
Course Requirements:
  • Internet connection
  • Lap Top or Desk Top
Tools:
  • Erwin
  • MS Word, MS Excel
  • My SQL
  • Windows Operating System
Training Certificates:
  • Will be provided.

Course Syllabus

 

Part 1 – Career Path of a Data Modeler

  • What is a Data Modeling?
  • Explanation of Data Modeler duties in brief
  • Certifications in Data Modeling
  • Career Path of a Data Modeler
  • Salary of a Data Modeler

Part 2 – Data Modeling Concepts

  • Who is a data modeler?
  • What are the other alternative titles for a Data Modeler?
  • What are the duties and responsibilities of a Data Modeler?
  • What is the difference between duty and responsibility?
  • What is a Data Model?
  • Who needs Data Modeling?
  • Different Data Modeling Tools
  • IDEF1X and IE Methodology

Part 3 – Data Modeling Types

  • Logical Data Model
  • Physical Data Model
  • Dimensional Data Model
  • Conceptual Data Model
  • Enterprise Data Model
  • Data Modeling Development Life Cycle

Part 4– Data Model Standards

  • Naming standards of objects
  • Abbreviating column names
  • Consistency in Column Names
  • Why it is important

Part 5 – Database Explanation from Data Modeling Perspective

  • Main object: Table, Column, Datatype
  • Constraints: NULL, NOT NULL, Primary Key, Unique, Check, Default Value
  • Other objects: Database, Schema, Tablespace, Segment, Extent, Privileges, Index, View, Synonym
  • DDL Statements: CREATE, ALTER, DROP
  • DML Statements: INSERT, UPDATE, DELETE

Part 6– How to create a logical Data Model

  • Entity, Attribute, Primary Key, Alternate Key, Inversion Key Entry, Rule, Relationship, Definition, Index, Unique Index

Part 7– Relationships

  • Identifying, Non-Identifying, Many to Many
  • Cardinality
  • One to One Relationship
  • One to many relationship
  • Many to many relationship
  • Whether Zero option is required or not
  • Resolving Many to Many Relationship
  • Self-Referential Integrity Relationship
  • Normalization process
  • Supertypes and Subtypes

Part 8 – How to create a Physical data model:

  • Table, Column
  • Primary Key Constraint, Unique Constraint Check Constraint, Foreign Key Constraint, Comment
  • Default Value
  • Unique Index, Non-Unique Index,
  • Difference between a logical data model and Physical Data Model

Part 9 – Physical Data Model, Database & Scripts:

  • What is Forward Engineering?
  • How to generate scripts from a data model and share it with DBA?
  • What is Reverse Engineering?
  • How to create a data model from a database?
  • How to create a data model from a script?
  • How to compare data models?
  • How to compare database and a data model?
  • What is subject area?
  • Why do we need so many subject areas?
  • How to implement Physical data model in a database?
  • How to generate SQL Code?
  • How to implement it in Database?

Part 9: Concepts: Dimensional Data Modeling, Data Warehouse and Data Mart

  • What is a Lookup?
  • How to maintain data in Lookups?
  • What is a Data Warehouse?
  • What is a Data Mart?
  • What is a Dimension?
  • What is Snow Flake Modeling?
  • What is Star Schema Modeling?
  • What is Slowly Changing Dimensions?
  • What is Dimensional Data Modeling?
  • How to create a data model for Data Warehouse and Data Mart?

Part 10:  Repository, Meta Data and Maintenance of the Data Model

  • What is a Repository?
  • What is Meta Data?
  • How to maintain the data model?
  • How to work in a multi-user environment