Practical Data Modelling
Data modelling is not optional; no database was ever built without at least an implicit model
- 4 live instructor sessions
- Case study videos
- Certificate of completion
Course objectives
On completion of this course, you will be able to:
- Articulate why data modelling is important
- Recognise the difference between a process model and a data model
- Recognise the different levels of abstraction of data model
- Build a data model
Benefits of this course
Gives you confidence to:
- Understand what a data model is saying
- Ask questions about a data model and its content
- Recognise if your data requirements are captured
- See how business data is implemented in technical solutions
Who is this course for?
It is targeted at anyone working in data requirements specification, data modelling or those working extensively with data modelling professionals and wish to be able to develop and review at more than just a basic level.
virtual
Data Modelling is the sharpest scope management tool available to information systems development.
- Virtual
- 4 x 4 hour session
- July dates: 12, 13, 19, 20
A$1900 +gst
Why data training?
- Organisations mandate training to protect valued assets: People, Money, Reputation and Data
- Helps staff Improve their understanding and confidence in handling data assets.
- "Organisations rely on their data assets to make more effective decisions and to operate more efficiently” (DMBoK V2 P20)
Course content
The course contains 2 sections:
- An introduction to data modelling
- Practical data modelling
An introduction to data modelling
The course teaches:
- How to read data models
- Modelling from primary sources
What to look for Rich in case studies the course also covers the areas of:
- Introduction to Relational modelling
- Removing redundancy through normalisation
- Entity Modelling
- Attribute modelling including data types, value range & codes
- Development of attributed conceptual models
- Practical development of a data model utilising example scenarios
Practical data modelling
The course expands on section 1 content with:
- More sophisticated modelling techniques
- Practical warehouse/lake modelling
- Larger case studies
The course also covers the areas of:
- Subtypes & Supertypes
- Roles
- Time & History
- Nulls, optional and completeness
- Types of identifiers (keys)
- De-normalisation
- Reuse, patterns & resources
- Data warehousing modelling (Star Schemes and an introduction to Vault models)
- Bringing together a number of skills by solving a specific tangible large case study
- No SQL considerations.
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