Data Modelling Course

Course Description

This course develops covers essential data modelling skills required by Data management specialists.
• Implement industry standard "best practice" approach to their Data Management work
• Understand the roles and skills required in database administration, data administration and repository administration
• Contribute to Technology Direction Evaluation
• Aid in the construction of a Data Charter
• Practise basic data administration, database administration and repository administration skills
• Develop procedures for managing an organisation's corporate data resource

Target Audience

Any delegate who wishes to acquire skills in data management.


There are no pre-requisites.

Length of Course
2 days basic£900 per delegate

3 days extended £1150 per delegate


The course will begin at 09.00 and end at 17.00.
There will be a lunch break and further short breaks in the morning and afternoon.

Course Objectives

The candidate will benefit from an in depth coverage of Data Modelling.

Course Topics

Course Outline
• Introduction to Data Modelling
o What is Data Modelling
o Types of Data Models; Network, Hierarchical, Relational, Dimensional
o What is an Enterprise Data Model and how to create it
o Why you should do data modelling and benefits
o Data Modelling Development Cycle; Conceptual, Logical and Physical Data Modelling
o Top-down vs. Bottom-up approach to Relational Data Modelling and how to select the right approach
o Key People involved in the Data Modelling
o What are Data Dictionaries, why and how you should create these
• Working as a Data Modeller
o Job description
o Key skills required
o Situation of current job market for data modellers
• Data Modelling Terminology
o Entities
o Tuples
o Attributes
o Relationships
o Keys
o Cardinality
o Participation
o Disjoint constraints
o Integrity constraints
• Data Modelling Notations
o Explanation of Entity Relationship (E-R) and Enhanced E-R Modelling, differences between these and when to create an Enhanced E-R Model
o Differences between some of the most popular notations including Information Engineering, Crow-feet, Chen Notation, IDEF1/IDEF1X, UML
• Normalisation
o How and when you should normalise a Relational Data model
• Denormalisation
o How and when you should denormalise a Relational Data model
• Relational Data Modelling
o How to gather requirements in order to create a Relational Data Model
o Comparison through an example of creating a Relational Data Model using Crow-feet and Information Engineering notation
o Step-by-step creation of a Relational Data Model using Information Engineering as the notation including naming conventions
o Best practices
• Dimensional Data Modelling
o Facts and Dimensions
o Types of fact tables and differences between them; Transaction, Snapshot and Accumulating Snapshot Facts
o Types of Dimensions including explanation of different types of Slowly Changing Dimensions
o Managing Slowly Changing Dimensions
o Different Dimensional Model Schemas and their benefits; Star, Snowflake, Starflake Schema
o How to gather requirements and identify dimensions and facts from the requirements
o Ways of improving database performance through materialised views, indexing, portioning
o Step-by-step creation of a Dimensional Data Model including naming conventions
o Best practices of Dimensional Data Modelling
• Comparison between Relational vs. Dimensional Data Models
• Data Modelling Tools suitable for all budgets
• Demo of a Data Modelling tool

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Data Modelling Course