Course Description

Entity relationship modelling, analysis class modelling and normalisation of data. They’re the three key approaches to analysing and modelling data, and they’re all covered in our course on Data Analysis.

You’ll also learn about approaches that reflect the business users’ needs and priorities and gain an overview of the more mechanistic approach offered by normalisation. And finally, we’ll explore the techniques used to validate data against stated requirements.

Target Audience

Business analysts, project managers, and solution developers who require a practical approach to analysing and modelling data. Data Analysis is also an Analytical Skills module on the BCS (ISEB) Advanced Diploma in Business Analysis.

Exam

During this two-day course, you’ll receive all the training you need to prepare for the BCS Professional Certificate in Data Analysis exam, which is held on the final afternoon of the course. A pass in this module will contribute to the BCS International Advanced Diploma in Business Analysis. The course is also consistent with SFIA skills DTAN levels 2 and 3.

The exam is included as part of the course cost. The multiple-choice exam consists of 40 questions with a pass mark of 26 out of 40. Currently, the exam must be taken remotely using the BCS online proctoring service.

Course Content

Data analysis for business analysts

• The importance of data to the organisation
• Data, information and business intelligence
• Applying data analysis to understand the organisation
• Using data analysis in business analysis

Principles of data analysis

• Rationale for analysing and modelling data
• Techniques used in data analysis
• Entity relationship diagrams
• Analysis class modelling
• Normalisation of data

Entity Relationship Diagrams

• Entities
• Attributes
• Relationships – cardinality and optionality
• Keys
• Entity and relationship descriptions
• Attributes and domains

Analysis class modelling

• Objects and classes
• Structure of a class: name, attributes, operations
• Associations and multiplicity
• Naming associations
• Generalisation

Normalisation of data

• Third normal form analysis rules
• Relations
• Primary and candidate keys
• Drawing third normal form models

Validating the data model

• Cross-reference matrices, CRUD matrix
• Data navigation paths