COURSE OUTLINE

1. 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