Online Data Modeling Training Syllabus

Online Data Modeling Training on

OLTP, Data Warehouse, Datamart, Dimensional and Snow Flake Data Modeling and Normalization. end to end process with ERWIN Tool

 

Course# 1 – Learn Erwin data modeling to create Data Models

Course Description:

This online course explains how to use Erwin Data Modeling tool to create logical data model, conceptual data model and physical data model. It also explains how to create different objects like entity, attribute, relationship, null, not null, primary key, foreign keys, naming conventions, one to one relationship, one to many relationship, many to many relationship, identical relationship, non-identical relationship, default, domain, subject area, reports generation etc.

Course Duration:

3 hours to 4 hours through SKYPE or Goto Meeting.

Course Fee:

60$ to 80$ per person

Data Modeling sample used:

Training Institute Data Model

Course Start Time:

Any time.

Course# 2 – Online Project assistance in Data Modeling

Course Description:

Understanding your project requirements, guiding you to create the right data models. Providing Solutions/Assistance to Data Modeling Projects!

Course# 3 – Online Advanced Data Modeling Training

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 @ 91-9080157239.

Course Information:

Data Model Samples Explained:

  1. Bank Data Model
  2. Training Institute Data Model
  3. Online purchase Data Model
  4. Doctor’s clinic Data Model
  5. Insurance Data Model
  6. Mortgage Data Model
  • Start Date: Batch I starts on 2nd June 2018 & Batch II starts on 23th June, 2018
  • Course Name: Advanced Data Modeling Training through SKYPE or GotoMeeting.
  • Course Fee: $175 (One hundred and seventy five US Dollars) per person:
  • Mode of Payment: To USA Savings Account or India Savings Account or through www.Xoom.com
  • Training Hours: 6.30 PM to 8.30 PM Indian Standard Time
  • Training Time: 2 to 2.5 hours per session
  • Days: Saturday & Sunday
  • Total no. of theoretical/Practical classes: At least 12 Hours
  • Mode of Teaching: Online through SKYPE
  • Instructor: Neelesh (American Citizen) & 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

 

Sample Data Models Covered:

  • Banking
  • Telecommunications
  • Staffing  etc.,

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 – 1NF, 2NF, 3NF
  • 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