Audience
This certification is relevant for anyone wishing to gain an understanding of the principles, rationale and techniques of data analysis, including data architects, business analysts, project managers, business change managers and business managers.
Prerequisites
There are no pre-requisites for entry to the examination.
Duration
2 Days.
Course Objectives
Upon completion of the certificate candidates will be able to demonstrate an understanding of:
- The basics of Data Analysis
- How to model data using class diagrams
- How to define data requirements
- The ways in which data is obtained and recorded
- How to analyse data for decision-making
- How data is protected
Course Content
1. Introduction to data
- Define the terms: data, data analysis, data model, information and business intelligence
- Distinguish between structured and unstructured data
- Explain the following data concepts:
- Conceptual, logical, physical data models
- Static and dynamic views
- Define the 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
- Define concepts and notations used in class diagrams
- Classes and objects
- The structure of a class: name, attributes, operations
- Modelling classes
- Associations
- Labelling associations
- Multiplicity
- Composition and Aggregation
- Attributes
- Interpret a class diagram
- Explain the use of generalisation in class diagrams
3. Defining data requirements
- Define data modelling concepts
- Metadata
- Domain definitions
- 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
- Third Normal Form data model
- Identify aspects of data quality
4. Obtaining and recording data
- Identify sources of data: surveys, sampling exercises, records
- Validate data models using a CRUD matrix
- Validate data models against requirements using
- Data Navigation Paths
5. Analysing data for decision-making
- 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; timeseries forecasting
- Explain data cleansing: rationale and key steps
- Interpret data using data analytics concepts
6. Protecting data
- Define data protection principles
- Define aspects relating to online data and ethics