Big Data Economics
| Code | School | Level | Credits | Semesters |
| ECON4060 | Economics | 4 | 15 | Spring UK |
- Code
- ECON4060
- School
- Economics
- Level
- 4
- Credits
- 15
- Semesters
- Spring UK
Summary
This module will focus on advanced Big Data methods and their applications in various economics problems. The planned topics of economics applications are naturally related to the research strength of the School of Economics, such as in international trade, macroeconomic prediction and labour economics. Topics of the module include nonlinear models, tree-based models, support vector machines, unsupervised learning and applications in international trade, household finance, macro forecasting, labour economics and text analysis.
Target Students
Available for students on the MSc Economics and Data Science degree and for PGT and PGR Economics students studying ECON4061 Machine Learning for Economics. Also available for students on the MSc Financial and Computational Mathematics degree.
Co-requisites
Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:
Classes
This module is delivered through a combination of lectures and computer classes.
Assessment
- 50% Coursework: Data Analysis Coursework
- 50% Exam (2-hour): Exam
Assessed by end of spring semester
Educational Aims
The aim of this module is to provide advanced theoretical training and up-to-date applications of Big Data methods for students, and to lay a modern basis for our PGT students to further pursue potential research degrees.Learning Outcomes
On completion of this module students should be able to demonstrate:
A2: An understanding of advanced theoretical methods
A3: An understanding of advanced quantitative methods
A4: An advanced knowledge of specialisms in economics, including the current state of research in that field
B1: Model the essential features of complex economic systems
B2: Use analysis, deduction and induction to solve economic problems
C1: Understand principles of research design and strategy
C2: Find and access economic data and evidence
C3: Apply appropriate quantitative methods (mathematical, statistical, graphical) to data and evidence
D1: Communicate effectively and clearly in written and/or oral formats
D2: Use appropriate IT packages effectively