BICSI DAV2 Valid Q&A - in .pdf

  • DAV2 pdf
  • Exam Code: DAV2
  • Exam Name: BCS Professional Certificate in Data Analysis V2.0
  • Updated: Oct 25, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: DAV2
  • Exam Name: BCS Professional Certificate in Data Analysis V2.0
  • DAV2 Online Test Engine
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  • If you purchase BICSI DAV2 Value Pack, you will also own the free online test engine.
  • Updated: Oct 25, 2026
  • Q & A: 0 Questions and Answers
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BICSI DAV2 Valid Q&A - Testing Engine

  • DAV2 Testing Engine
  • Exam Code: DAV2
  • Exam Name: BCS Professional Certificate in Data Analysis V2.0
  • Updated: Oct 25, 2026
  • Q & A: 0 Questions and Answers
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BICSI DAV2 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Introduction to business data10%- Stages of the data lifecycle
  • 1. Using data for business decisions and operations
    • 2. Recording data
      • 3. Obtaining data
        • 4. Removing data
          • 5. Modelling data requirements
            • 6. Identifying data sources
              - Conceptual, logical and physical data models
              - Structured and unstructured data
              - Static and dynamic views
              - Definitions of data, data analysis, data models, information and business intelligence
              Topic 2: Defining data requirements15%- Data quality
              - Data modelling concepts
              • 1. Domain definitions
                • 2. Metadata
                  - Data normalisation
                  • 1. Rationale for data normalisation
                    • 2. Simple, compound, hierarchical and foreign keys
                      • 3. First normal form, second normal form and third normal form
                        • 4. Unnormalised form
                          Topic 3: Modelling data using class diagrams35%- Interpreting class diagrams
                          - Generalisation in class diagrams
                          - Concepts and notations used in class diagrams
                          • 1. Modelling classes
                            • 2. Multiplicity
                              • 3. Class structure, including names, attributes and operations
                                • 4. Composition and aggregation
                                  • 5. Attributes
                                    • 6. Labelling associations
                                      • 7. Classes and objects
                                        • 8. Associations
                                          Topic 4: Obtaining and recording data10%- Validating data models using a CRUD matrix
                                          - Sources of data
                                          • 1. Surveys
                                            • 2. Sampling exercises
                                              • 3. Records
                                                - Validating data models against requirements using data navigation paths
                                                Topic 5: Analysing data for decision-making25%- Interpreting data using data analytics concepts
                                                - Data cleansing, including rationale and key steps
                                                - Data analytics concepts and applications
                                                • 1. Dataset calculations, including counts, totals, averages and probabilities
                                                  • 2. Validating datasets, including confirmation bias, sample size, outliers and consistency
                                                    • 3. Obtaining datasets, including context, source and lineage
                                                      • 4. Data relationships, including regression analysis, correlation, causation and time-series forecasting
                                                        Topic 6: Protecting data5%- Data protection principles
                                                        - Online data and ethics
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