Anyone wishing to gain an understanding of the benefits and uses of data analysis and the techniques applied when analysing business data. In particular, business analysts, systems analysts and technical or solution architects.
There are no prerequisites for this course.
Two days. Lecture presentations are supported by group practical work which allows discussion, reinforcement of learning and enhancement of the understanding process.
This course is available on site only. Please call for details.
This course leads to the British Computer Society Professional Certificate in Data Analysis. The certificate comprises fundamental principles, concepts and techniques used to identify, analyse and model data. The aim of this certification is to enable candidates to define data requirements with detailed understanding and rigour. The course content conforms to the Professional Certificate in Data Analysis syllabus version 1.2 and the main objective of the course is to help participants gain a successful outcome in the certificate examination.
At the end of the course, delegates will be able to:
- Define the terms identified in the syllabus topic area.
- Explain the purpose of data analysis and modelling.
- Distinguish between different types of data analysis artifacts.
- Identify constructs used within data models, both entity relationship and analysis class models.
- Distinguish between entity types and entity occurrences: objects and classes.
- Interpret data model extracts.
- Evaluate conformance between data analysis artifacts and requirements.
- State the business rules defined within data analysis artifacts.
- Define the rules used to derive third normal form relations from un-normalised data sources.
- Evaluate data sets against normalisation rules.
Concepts and Principles of Data Analysis and Modelling
Definitions of terms – data, data analysis, data model
Rationale for analysing and modelling data
Techniques used in data analysis:
• Entity relationship modelling
• Analysis class modelling
Approaches to data analysis and modelling:
• Derived from business needs: ‘top-down’
• Derived from data sources: ‘ bottom-up
Application of data analysis artefacts:
• Business modelling
• System modelling (existing and required)
• Impact analysis
• Communication and training
Entity Relationship Modelling
Content of an entity relationship model
Identification of entities and attributes
Entity types and entity occurrences
Attribute types and attribute occurrences
Simple and compound keys
• Cardinality (1:1, 1:M, M:M)
• Naming relationships
Super-types and sub-types
Normalisation process and rules:
• Unnormalised (UNF)
• First normal form (FNF)
• Second normal form (SNF)
• Third normal form (TNF)
Definition of the TNF tests
Rationalisation of TNF results from multiple data sources
Development of the TNF model
Analysis Class Modelling
Objects and classes
Structure of a class: name, attributes, operations
Associates and multiplicity
Cross-referencing matrices: CRUD matrix
Data navigation paths