BCS DA01 : BCS Professional Certificate in Data Analysis

DA01 real exams

Exam Code: DA01

Exam Name: BCS Professional Certificate in Data Analysis

Updated: Jul 26, 2026

Q & A: 0 Questions and Answers

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About BCS DA01 Exam

The BCS Professional Certificate in Data Analysis certification program provides a comprehensive understanding of data analysis and covers a wide range of topics, including data mining, data visualization, statistical analysis, and predictive modeling. The program is designed to provide participants with practical skills that can be applied in their work environment. The curriculum is structured in a way that ensures that participants get a thorough understanding of the concepts and techniques used in data analysis.

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The DA01 certification program is ideal for professionals who are looking to transition into a career in data analysis or data science. The program is specially tailored for individuals who want to learn how to identify trends, patterns, and relationships in data sets. After completing the program, learners acquire the ability to make data-driven conclusions and devise effective solutions to complex business problems. The BCS DA01 certification is ideal for professionals looking to establish themselves as experts in data analysis or data science.

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BCS DA01 Exam Syllabus Topics:

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

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