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BCS DA01 - BCS Professional Certificate in Data Analysis

Updated: Jul 27, 2026

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DA01 Braindumps VCE
  • Exam Code: DA01
  • Exam Name: BCS Professional Certificate in Data Analysis

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BCS DA01 Exam Syllabus Topics:

SectionWeightObjectives
Defining Data Requirements15%- Data definitions
- Metadata
  • 1. Descriptive metadata
    • 2. Statistical metadata
      • 3. Structural metadata
        - Domain definitions
        - Data quality
        - Normalisation
        • 1. Third normal form model
          • 2. Third normal form
            • 3. Second normal form
              • 4. Un-normalised form
                • 5. First normal form
                  - Relational data theory
                  • 1. Two-dimensional structures
                    • 2. Primary keys
                      • 3. Compound and concatenated keys
                        • 4. Foreign keys
                          Modelling Data Using Class Diagrams35%- Naming conventions
                          - Classes and objects
                          - Generalisation
                          - Associations and multiplicity
                          • 1. One-to-many relationships
                            • 2. Resolving many-to-many relationships
                              • 3. Multiple roles
                                • 4. One-to-one relationships
                                  • 5. Many-to-many relationships
                                    - Attributes
                                    - Aggregation and composition
                                    - Class diagrams
                                    Analysing Data for Decision-Making25%- Dataset calculations
                                    • 1. Mean
                                      • 2. Counting
                                        • 3. NULL values
                                          • 4. Totalling
                                            • 5. Maximum and minimum
                                              • 6. Median
                                                • 7. Mode
                                                  • 8. Probability
                                                    - Sourcing datasets
                                                    • 1. Data lineage
                                                      - Data analytics process
                                                      - Validating and cleansing datasets
                                                      • 1. Outliers
                                                        • 2. Confirmation bias
                                                          • 3. Consistency
                                                            • 4. Sampling
                                                              - Identifying meaningful relationships
                                                              • 1. Correlation and causation
                                                                • 2. Regression analysis
                                                                  • 3. Time-series analysis and forecasting
                                                                    - Interpreting results
                                                                    Protecting Data5%- Ethical use of data
                                                                    - Data protection principles
                                                                    Obtaining and Recording Data10%- Data navigation paths
                                                                    - Data navigation diagrams
                                                                    - Identifying sources of data
                                                                    - CRUD matrix validation
                                                                    Introduction to Data10%- The data lifecycle
                                                                    - Structured and unstructured data
                                                                    - Initial concepts and terminology
                                                                    • 1. Data analysis versus data analytics
                                                                      • 2. Information versus data
                                                                        - Data modelling and data models
                                                                        • 1. Conceptual, logical and physical data models
                                                                          • 2. Static and dynamic views of data

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