IB9HI-15 Case Studies in Data Science and Economics
Introductory description
The module aims to provide training in applying data science methods to contemporary economic problems by looking at case studies.
Module aims
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.
- Data visualisation and house price fluctuations
- Causation, correlation and the statistical links between asset prices and recessions
- Big data and international currency movements
- The difficulties of measuring latent economic variables (eg the output gap) and uncertainty quantification
- Automated monetary policy bots
- 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
- Demonstrate understanding of 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
- 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
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
Interpret evidence
Transferable skills
Written communication.
Problem solving.
Study time
| Type | Required |
|---|---|
| Online learning (scheduled sessions) | 10 sessions of 1 hour (7%) |
| Other activity | 16 hours (11%) |
| Private study | 50 hours (33%) |
| Assessment | 74 hours (49%) |
| Total | 150 hours |
Private study description
Private study to include preparation for lectures
Other activity description
8 x 2 hrs face-to-face workshops
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 A4
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Individual Assignment | 100% | 74 hours | Yes (extension) |
Reassessment component is the same |
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Feedback on assessment
Comments on group work and individual assignments
Pre-requisites
Students should be alerted to the fact that they will need intermediate MS Excel
Courses
This module is Optional for:
- Year 1 of TIBS-H60Z MSc Financial Technology
- Year 1 of TIBS-N500 MSc in Marketing and Strategy
- Year 1 of TIBS-N1N3 Postgraduate Taught Business Analytics
- Year 1 of TIBS-N1F5 Postgraduate Taught Business and Finance
- Year 1 of TIBS-N1F2 Postgraduate Taught Business with Consulting
- Year 1 of TIBS-N1F3 Postgraduate Taught Business with Marketing
- Year 1 of TIBS-N1QG Postgraduate Taught Business with Operations Management
- Year 1 of TIBS-N1F4 Postgraduate Taught International Business (MINT)
- Year 1 of TIBS-N2N3 Postgraduate Taught Management