The certificate is divided into 10 units, covering the following:
| Course unit | Learning topics |
|---|---|
| 1: The CRISP framework for data analytics | a) Business understanding b) Data understanding c) Data preparation d) Data modelling e) Data evaluation f) Deployment |
| 2: Big data and data analytics | a) What is big data? b) The 3 Vs of big data c) The value and lessons to be learned from big data |
| 3: Sources of data | a) Interal b) External |
| 4: Types of analytics | a) Descriptive analytics b) Predictive analytics c) Prescriptive analytics |
| 5: Data analytics methodologies | a) Robotics b) Artificial intelligence c) Machine learning |
| 6: Mainstream tools and key applications of data analytics | a) Tool and applications for descriptive analytics b) Tools and applications for predictive analytics c) Tools and applications for prescriptive analytics |
| 7: Data visualisation and communication | a) What is data visualisation? b) The purpose of data visualisation c) The benefits of data visualisation d) The history of data visualisation e) Types of visualisation - comparison f) Types of visualisation - composition g) Types of visualisation - relationship h) What makes good visualisation? |
| 8: Scepticism in data analytics | |
| 9: Ethical considerations in the use of data | |
| 10: End of units data analysis activity |