Introductory description

The module aims to provide training in applying data science methods to contemporary economic problems by looking at case studies.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

There will be four case studies from the following list of six. Topics will be rotated through successive years.

1. Data visualisation and house price fluctuations
2. Causation, correlation and the statistical links between asset prices and recessions
3. Big data and international currency movements
4. The difficulties of measuring latent economic variables (eg the output gap) and uncertainty quantification
5. Automated monetary policy bots
6. Textual data recognition and predicting FX rates

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of the importance of data science methods through contemporary economic applications.
• Understand better the economic issues raised in the case studies (including foreign exchange determination, interest rate prediction, asset prices, house prices, monetary policy).
• Demonstrate considered thinking about theoretical concerns.
• Demonstrate interpretation of evidence skills.
• Demonstrate ability to find and explain a narrative that blends theoretical and empirical issues.

Indicative reading list

Silver, N. (2013) The Signal and The Noise: The art and science of prediction, Penguin.
Begg, D.; Vernasca, G.; Fischer, S. and Dornbusch, R. (2014) Economics (11th ed) McGraw-Hill Education (UK) Ltd.

Subject specific skills

Appreciate and be able to utilise a variety of tools from data science (including data visualization, statistical testing, machine learning, big data methods).
Use economic thinking to analyse contemporary issues.

Transferable skills

Written communication.
Problem solving.