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BCS DA01 Braindumps - in .pdf Free Demo

  • Exam Code: DA01
  • Exam Name: BCS Professional Certificate in Data Analysis
  • Last Updated: Aug 23, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: DA01
  • Exam Name: BCS Professional Certificate in Data Analysis
  • Last Updated: Aug 23, 2026
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BCS DA01 Exam Syllabus Topics:

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

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