1. Introduction to Business Data (10%: K2) 1.1 Define the terms:

• Data
• Data analysis
• Data model
• Information
• Business intelligence

1.2 Distinguish between:

• Structured and unstructured data

1.3 Explain data concepts:

• Conceptual, logical, physical data models
• Static and dynamic views

1.4 Define stages in the data lifecycle:

• Identifying data sources
• Modelling data requirements
• Obtaining data
• Recording data
• Using data for business decisions and operations
• Removing data

2. Modelling Data Using Class Diagrams (35%: K4) 2.1 Define concepts and notations used in class diagrams:

• Classes and objects
• Structure of a class: name, attributes, operations
• Modelling classes
• Associations
• Labelling associations
• Multiplicity
• Composition and Aggregation
• Attributes

2.2 Interpret a class diagram 2.3 Explain the use of generalisation in class diagrams

3. Defining Data Requirements (15%: K3) 3.1 Define data modelling concepts:

• Metadata
• Domain definitions

3.2 Explain data normalisation:

• Rationale for data normalisation
• Unnormalised form
• First normal form, second normal form, and third normal form relations
• Simple, compound, hierarchic, and foreign keys

3.3 Identify aspects of data quality

4. Obtaining and Recording Data (10%: K3) 4.1 Identify sources of data:

• Surveys
• Sampling exercises
• Records

4.2 Validate data models using a CRUD matrix 4.3 Validate data models against requirements using Data Navigation Paths

5. Analysing Data for Decision-Making (25%: K4) 5.1 Explain and apply data analytics concepts:

• Obtaining the data set: context, source, and lineage
• Validating the data set: confirmation bias, sample size, outliers, consistency
• Dataset calculations: counts, totals, averages, probabilities
• Data relationships: regression analysis; correlation and causation; time-series forecasting

5.2 Explain data cleansing: rationale and key steps 5.3 Interpret data using data analytics concepts

6. Protecting Data (5%: K2) 6.1 Define data protection principles 6.2 Define aspects relating to online data and ethics

Professional Certificate in Data Analysis Course Prerequisites

• Basic knowledge in statistics and mathematics
• Understanding of data manipulation and data analysis techniques
• Proficiency in a programming language such as Python or R
• Familiarity with SQL for database management
• Experience with data visualization tools such as Tableau or Excel.