BCS DA01 dumps - in .pdf

DA01 pdf
  • Exam Code: DA01
  • Exam Name: BCS Professional Certificate in Data Analysis
  • Updated: Sep 07, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: DA01
  • Exam Name: BCS Professional Certificate in Data Analysis
  • Updated: Sep 07, 2026
  • Q & A: 0 Questions and Answers
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BCS DA01 dumps - Testing Engine

DA01 Testing Engine
  • Exam Code: DA01
  • Exam Name: BCS Professional Certificate in Data Analysis
  • Updated: Sep 07, 2026
  • Q & A: 0 Questions and Answers
  • Software Price: $888.00
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BCS DA01 Exam Syllabus Topics:

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

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