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Module Catalogue

IB9HI-15 Case Studies in Data Science and Economics

Department
Warwick Business School
Level
Taught Postgraduate Level
Module leader
Shaun Vahey
Credit value
15
Module duration
1 week
Assessment
100% coursework
Study location
University of Warwick main campus, Coventry

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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

The module aims to provide training in applying data science methods to contemporary economic problems by looking at case studies. Specifically, the module aims to give students the opportunity to conduct a data science project using economic data. The students will discover how to replicate and extend a data science case. The module aims to give them the appropriate grounding in economic analysis, while developing their research and communication skills. A formative group exercise will be set whereby groups will be given data and asked to conduct a statistical analysis of the economic problem. This will serve as a "dummy run" for the individual assignment, and there will be in-class feedback.

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.

Study time

Type Required
Lectures 10 sessions of 3 hours (38%)
Private study 48 hours (62%)
Total 78 hours

Private study description

Private study to include preparation for lectures

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group A1
Weighting Study time Eligible for self-certification
Assessment component
Individual Assignment 100% 72 hours Yes (extension)
Reassessment component is the same
Feedback on assessment

Feedback through MyWBS

Pre-requisites

Students should be alerted to the fact that they will need intermediate MS Excel

There is currently no information about the courses for which this module is core or optional.