Data Science for Economics and Finance

• Credit value: 30 credits at Level 6
• Convenor: Professor Sandeep Kapur
• Tutors: Dr Daniel Kaliski, Dr Ilaria Peri
• Assessment: a two-hour 10-minute examination (70%) and data-based project (30%)

While economic analysis of data to discover causal relations has been a long-standing tradition in economics and finance, the landscape has been altered by recent trends - the emergence of ‘big data’, the ease of gathering data through ‘webscraping’, use of machine learning for predictive analysis in economic and business contexts. The ability to use open-source programming languages such as R and Python can provide economics graduates with skills that are highly valued in the job market.

In this module you will learn the basic programming skills in languages such as R and Python, and how analysis of big data and machine learning can be useful in the design of economic policy and in the understanding of financial markets.

Indicative syllabus

Programming and data

• Intro to data science: key issues and concepts; getting started with programming
• Boolean logic; conditionals; wildcards
• Loops; combining loops with conditionals
• Getting data (webscraping)
• Visualisation of data and statistical results

Introduction to machine learning

• LASSO, ridge regression; discussion of overfitting, bias-variance trade-off
• Discrete outcomes; logit and probit
• Classifiers; decision trees

By the end of this module, you will:

• understand the basic programming and construct simple code
• be able to use webscraping techniques to gather data from diverse sources
• be able to deploy standard programmes to visualise data and perform statistical analysis
• understand and be able to apply simple machine learning techniques.