Economics

Economics and Data Science

Module code: L1100
Level 6
15 credits in spring semester
Teaching method: Lecture, Seminar
Assessment modes: Computer based exam, Coursework

Big data refers to the increasing availability of vast amounts of data from various facets of human behaviour, and the concomitant growth in computational power and statistical techniques (machine learning) to analyse these data to detect patterns and draw useful inferences for business and policy purposes. Big data is transforming decision making in many domains. Economists have conventionally relied on data sources such as household and firm surveys, administrative data, and the occasional census. Machine learning has broadened the scope of empirical work, e.g. analyses of satellite imagery, text data from social media, retail scanner data, credit card and mobile phone usage etc. Empirical economics has conventionally focussed on causal inference. In contrast, machine learning emphasises prediction which is a valuable tool in many policy and business settings. These recent developments in machine learning have resulted in many new applications that we see on a daily basis For example, facial recognition software, email spam filtering and the development of “personal assistants” such as Siri and Alexa. In this module we will focus on the use of Big Data and machine learning techniques in the field of Economics.

Module learning outcomes

• Display a systematic understanding of methods appropriate to the analysis of big data with respect to problems in economics.
• Demonstrate the ability to carry out self-directed study and research, understand the limitations and be able to comment upon research outputs.
• Demonstrate a high level of competence in the use of an appropriate range of computer software, particularly relating to the analysis of big data.
• Demonstrate an ability to communicate advanced concepts and information to specialist and non-specialist audiences.